A PSYCHOPHYSICAL APPROACH FOR PREDICTING ISOMETRIC AND
ISOTONIC HAND MUSCLE STRENGTH IN THE AVIATION INDUSTRY
BY
HESHAM A. ALMOMANI
BS, Yarmouk University, 1988
MSA, Central Michigan University, 2005
MAS, Embry-Riddle Aeronautical University, 2007
DISSERTATION
Submitted in partial fulfillment of the requirements for
the degree of Doctor of Philosophy in Industrial & Systems Engineering
in the Graduate School of
Binghamton University
State University of New York
2015
© Copyright by Hesham Al-Momani 2015
All Rights Reserved
Accepted in partial fulfillment of the requirements for
the degree of Doctor of Philosophy in Industrial & Systems Engineering
in the Graduate School of
Binghamton University
State University of New York
2015
November 20, 2015
Dr. Mohammad T. Khasawneh, Committee Chair and Faculty Advisor
Department of Systems Science & Industrial Engineering, Binghamton University
Dr. Krishnaswami "Hari" Srihari, Comittee Member
Thomas J. Watson School of Engineering and Applied Science, Binghamton University
Dr. Nagen Nagarur, Committee Member
Department of Systems Science & Industrial Engineering, Binghamton University
Dr. Harold W. Lewis III, Committee Member
Department of Systems Science & Industrial Engineering, Binghamton University
Dr. Roy T.R. McGrann, Outside Examiner
Department of Mechanical Engineering, Binghamton University
iii
ABSTRACT
In the aviation industry, most operations are accomplished using hands. Hand grip
strength is a significant factor that can influence human performance in terms of the
amount of force that an individual can apply and their time endurance limit. The main
objective of this study is to determine the maximum voluntary contraction and fatigue
endurance limits for both types of hand muscles (isometric and isotonic) for workers in
the Jordanian aviation industry. Using a psychophysical approach based on human
subjective perception of fatigue, a total number of 132 (aged between 20 and 60 years
old) subjects from the aviation industry was studied. The experiment investigates the
effect of nine different factors on three responses: maximum voluntary contraction
(MVC), isometric endurance limit, and isotonic endurance limit, and the relationships
between them. In addition, general and specific predictive linear models were developed
where not all factors are included simultaneously. The predictor variables are age, hand
dominancy, human body posture, grip circumference (GC), forearm circumference (FAC),
body mass index (BMI), height, profession (trade) and smoking condition. The isometric
endurance limit tested for different percentages of MVC at 20%, 40%, 60% and 80%,
which reflects real-life situations. The isometric endurance limit was tested for those
between 20% and 60% of the MVC force. In this experiment, digital hand grip
dynamometer was used to increase the accuracy of the experiment. The research
experiment outputs were analyzed with statistical analysis (e.g., descriptive statistical
analysis, interval plots, model adequacy checks, residual plots, MANOVA and ANOVA).
iv
Mathematical modeling (linear and nonlinear) and machine learning techniques
(Artificial Neural Networks (ANNs), Artificial Neuro Fuzzy Inference System (ANFIS))
were applied. Results show that age and physical factors have significant effects. All
predictive models compared on the R-squared values and Root Mean Square Error
(RMSE). The machine learning models obtained the lowest RMSE (7.09 e -8 - 9.9 e-1)
and provided the better fit for the data than the mathematical models, especially ANFIS
methodology; however, linear models were convenient to build for this research. A pilot
study was conducted to refine the best framework for the actual experiment. Research
findings can be applied to the employment process of aviation industry workers as well as
to workers of police, firefighting, and air force to enhance general health of athletic
personnel and for better design tasks and related tools in a more economical way.
v
DEDICATION
In the name of Allah, the most beneficent, the most merciful, this dissertation is
dedicated to the following people: First my father (‫)رحمه هللا‬, my mother, wife and
my family for their endless encouragement, love and support, without their prayers,
I would never have gotten to this stage of academic development. Second for those
nation figures, distinctive, unequaled MEN, Major Generals Basha’s his excellency
Atif Altel, Engineer Faith Zael Bani Saker, Pilot Mohamad Alomari, Pilot Hilal
Faraj Alnajar, Judge Ziad Edwan and
Pilot Hashim Al-momani), ex-senator
Samih Al-momani, dearest friends Engineer Hasan Mobideen, Naser Batayneh
and Mwafaq Alzobi, and finally the Hughes 203 team, All instilled me with the
strength, values, principles, and discipline with which to succeed in any task big or
small and who have always believed in me and inspire me to be who I am today.
vi
ACKNOWLEDGEMENTS
I wish to thank Dr. Mohammad T. Khasawneh, who never lost his patience with me
during the very difficult time in the last seven years for his guidance and support
throughout my study and research. I am also exceedingly grateful to Professor
Khasawneh and Vice Provost for International Affairs, Dean and University
Distinguished Professor Krishnaswami "Hari” Srihari who both inspired me to be what I
am today. My appreciation is also extended to Professors Nagen Nagarur and Harold W.
Lewis III for their kindness and help during my study. Special thanks go to all my friends
at Royal Jordanian Air Force (officers and NCOS) who have assisted me in my study and
experimentations. I am exceedingly grateful to Professor Mohammad T. Khasawneh for
his guidance and support throughout my whole doctoral program. As a mentor, his
dedication to his students is unsurpassed.
vii
Table of Contents
Section
Page
List of Tables
List of Figures
Chapter One Introduction
1.1 Work Related Musculoskeletal Disorders
1.2 Human Muscle Fatigue
1.3 Human Grip Strength
1.4 Maximum Voluntary Contraction
1.5 Problem Statement
1.6 Research Objectives
1.7 Research Significance
1.8 Dissertation Organization
Chapter Two Literature Review
2.1 Maximum Voluntary Contraction
2.2 Isometric Endurance Limit
2.3 Isotonic Muscle Fatigue
2.4 Isokinetic Muscle Fatigue
2.5 Grip Strength New Research Areas
Chapter Three Research Methodology
3.1 Introduction
3.2 Experiment Elements
3.3 Experimental Procedure
3.4 Data Modeling and Analysis
Chapter Four Analysis and Discussion
4.1 Introduction
4.2 Descriptive Statistics
4.3 Multivariate Analysis Of Variance (MANOVA)
4.4 Basic Analysis
4.5 Maximum Voluntary Contraction
4.6 Isometric Endurance Limit
4.7 Isotonic Endurance Limit
4.8 Neural Network Analysis
4.9 ANFIS Neural Network Analysis
Chapter Five Conclusions and Future Work
5.1 Mathematical Modeling Conclusion
5.2 Neural Network Analysis Conclusion
5.3 ANFIS Neural Network Analysis Conclusion
5.4 Future Work
Appendices
References
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LIST OF TABLES
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Table 1-1 Independent Variables
Table 2-1 Grip Strength Value For Middle Aged Females
Table 2-2 2 Maximum voluntary for Standing and Sitting and Dominant Hand
Table 2-3 MVC Regression Models for MVC
Table 2-4 MVC Fractions with Wrist Posture Effect
Table 3-1 Descriptive Statistics of Aviation Male Subjects
Table 3-2 Dependent and Independent Variables and Treatment levels
Table 3-3 Overall Research Methodology for Aviation Subjects
Table 3-4 Data Analysis and Modeling Methodology
Table 4-1 Dependent and Independent Variables with Their Levels
Table 4-2 Overall Summary Data
Table 4-3 Descriptive Statistics (Dependent Factors)
Table 4-4 MANOVA for Experiment Terms
Table 4-5 MANOVA for all Dependent Factors
Table 4-6 Factor Information for ANOVA General Factorial Regression
Table 4-7 ANOVA General Factorial Regression
Table 4-8 MVC General Linear, Nonlinear Models (MATLAB 15)
Table 4-9 MVC General Linear Models (Detailed) (MATLAB 15)
Table 4-10 MVC General Non-Linear Models (detailed) (MATLAB 15)
Table 4-11 RMSE Values (Linear and Non-Linear) Regression
Table 4-12 MVC Values for Posture (Standing and Sitting)
Table 4-13 MVC Values for Strongest Age Periods
Table 4-14 Descriptive Statistics for Jordanian Subjects
Table 4-15 Descriptive Statistics: MVC Values for Different Races
Table 4-16 Factor information for ANOVA General Factorial Regression
Table 4-17 ANOVA General Factorial Regression: Isometric En 20%
Table 4-18 ANOVA General Factorial Regression: Isometric En 40%
Table 4-19 ANOVA General Factorial Regression: Isometric En 60%
Table 4-20 ANOVA General Factorial Regression: Isometric En 80%
Table 4-21 ANOVA Significant Factors
Table 4-22 ANOVA Interaction Factors
Table 4-23 Isometric Endurance Limit General Linear Models
Table 4-24 Isometric Endurance Limit Non Linear Regression
Table 4-25 Isometric Endurance Limit RMSE Values Linear and Non-linear Models
Table 4-26 Means for Isometric Endurance Limit for different Age groups
Table 4-27 Anthropometric Data for Jordanian Subjects
Table 4-28 Descriptive Statistics: Isometric End, Limit
Table 4-29 ANOVA General Factorial Regression
Table 4-30 Isotonic Endurance Limit General Linear and Nonlinear Models
Table 4-31 Isotonic Endurance Limit General Linear Models (MATLAB 15)
Table 4-32 Isotonic Endurance Limit General Nonlinear Models (MATLAB 15)
Table 4-33 RMSE Values Isotonic Endurance Limit Linear and Non-Linear regression
Table 4-34 Isotonic Endurance Limit Descriptive Statistics
Table 4-35 Summary Isotonic Endurance Limit Vs Age
Table 4-36 Summary of FAC Effect in Isotonic Endurance Limit Test
Table 4-37 Anthropometric Data
Table 4-38 General Linear Models for Isotonic Endurance Limit
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Table 4-39 Nonlinear Regression Models for Isotonic Endurance Limit
Table 4-40 Summary of Neural Network Performance (MVC, Isometric and Isotonic
Endurance Limits)
Table 4-41 Neural Network Performance for MVC Test
Table 4-42 Neural Network Performance for Isometric Endurance Limit
Table 4-43 Neural Network Performance for Isotonic Endurance Limit
Table 4-44 Neural Network Performance for the Three Tests
Table 4-45 Neural Network Error Histogram
Table 4-46 Neural Network Function Fit Plot
Table 4-47 Neural Network Regression Plots for the Three Tests
Table 4-48 ANFIS Output Errors for the Three Tests (MVC, Isometric and Isotonic
Endurance Limits)
Table 4-49 ANFIS Output Errors for Each Experimental Condition
Table 5-1 General Linear and Nonlinear Models for MVC Test (MATLAB 15)
Table 5-2 Posture Effect on MVC
Table 5-3 Age Effect on MVC
Table 5-4 Height Effect on MVC
Table 5-5 BMI Effect on MVC
Table 5-6 Hand GRIP Circumference (HGC) Effect on MVC
Table 5-7 Forearm Circumference (HGC) Effect on MVC
Table 5-8 Trade Effect on MVC
Table 5-9 Race Effect on MVC
Table 5-10 Smoking Effect on MVC
Table 5-11 Dominancy Effect on MVC
Table 5-12 General Linear Models for Isometric Endurance Limit
Table 5-13 Isometric Endurance Limit Nonlinear Regression
Table 5-14 Age Effect on Isometric Endurance Limit
Table 5-15 Height Effect on Isometric Endurance Limit
Table 5-16 BMI Effect on Isometric Endurance Limit
Table 5-17 Hand Grip Circumference (HGC) Effect on Isometric Endurance Limit
Table 5-18 Forearm Grip Circumference (HGC) Effect on Isometric Endurance Limit
Table 5-19 TRADE Effect on Isometric Endurance Limit
Table 5-20 Isometric Endurance Limit for Jordanian Subjects
Table 5-21 Smoking Effect on Isometric Endurance Limit
Table 5-22 Hand Dominancy Effect on Isometric Endurance Limit
Table 5-23 Isotonic Endurance Limit General Linear and Nonlinear Model
Table 5-24 Age Effect on Isotonic Endurance Limit
Table 5-25 Height Effect on Isometric Endurance Limit
Table 5-26 BMI Effect on Isometric Endurance Limit
Table 5-27 Hand Grip Circumference (HGC) Effect on Isometric Endurance Limit
Table 5-28 Forearm Effect on Isometric Endurance Limit
Table 5-29 Trade Effect on Isometric Endurance Limit
Table 5-30 Isometric Endurance Limit for Jordanian Subjects
Table 5-31 Smoking Effect on Isometric Endurance Limit
Table 5-32 Hand Dominancy Effect on Isometric Endurance Limit
Table 5-33 Neural Network Summary (MVC, Isometric and Isotonic Endurance
Limits)
Table 5-34 ANFIS Output Errors for the Tests (MVC, Isometric and Isotonic
Endurance Limits)
Table 5-35 ANFIS Output Errors for Each Experimental Condition
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LIST OF FIGURES
Page
Figure 1-1 Carpal Tunnel Syndrome
Figure 1-2 Lateral Epicondylitis
Figure 1-3 Work Related Musculoskeletal Disorders
Figure 1-4 MSDs Injuries and Illnesses Numbers for Year 2010 (BLS, 2010)
Figure 1-5 QEC Assessment Form
Figure 1-6 Median Days Away From Work and Incidence Rate Due To
Injuries and Illness by Nature 2010 (BLS, 2010)
Figure 1-7 Number of Sprain, Strain, and Tear Cases Requiring Days Away
From Work by Selected Part of Body (BLS, 2010)
Figure 1-8 Average Days Away from Work
Figure 1-9 Muscles Involved in Grip Strength (Vansuh, 2012)
Figure 2-1 Males MVC with Age (Chatterjee & Chowdhuri, 1991)
Figure 2-2 Various Hand Wrist Postures Used (Khan, 2010)
Figure 2-3 Endurance Limit vs. MVC% (Different Shoulder Posture)
Figure 2-4 Endurance Limit for Different % of MVC
Figure 2-5 Endurance Limit Of 40% of MVC
Figure 2-6 Endurance Limit Of 40% of MVC of Left Hand and Right Hands
Figure 3-1 Experiment Instruments
Figure 3-2 Subject Posture during the Tests
Figure 4-1 Residuals Plots for MVC
Figure 4-2 MVC Models (Chatterjee & Chowdhuri, 1991)
Figure 4-3 MVC Posture effect (D)
Figure 4-4 MVC Posture effect (ND)
Figure 4-5 Relationship between MVC and Age for Different Posture and
Hand Dominancy
Figure 4-6 Relationship between MVC and Age
Figure 4-7 Relationship between MVC and Height
Figure 4-8 Relationship between MVC and BMI
Figure 4-9 Relationship between FAC and MVC
Figure 4-10 Relationship between Trade and MVC for Different Posture and
Dominancy
Figure 4-11 Relationship between MVC and Race (Male)
Figure 4-12 Relationship between MVC and Smoking
Figure 4-13 Relationship between Hand Dominancy and MVC for Different
Age Groups, Hand Dominancy, and Posture
Figure 4-14 Residual plots for isometric endurance limit test
Figure 4-15 Relationship between Isometric Endurance Limit and Age
Figure 4-16 Relationship between Isometric Endurance Limit and Height
Figure 4-17 Relationship between Isometric Endurance Limit and BMI
Figure 4-18 Relationship between Isometric Endurance Limit and HGC
Figure 4-19 Relationship between Isometric Endurance Limit and FAC
Figure 4-20 Relationship between Isometric Endurance Limit and Trade
Figure 4-21 Relationship between Isometric Endurance Limit and Smoking
Figure 4-22 Relationship between Isometric Endurance Limit and Dominancy
Figure 4-23 Residual Plots for Isotonic Endurance Limit
Figure 4-24 Relationship between Age and Isotonic Endurance Limit for
Different Speed and Dominancy
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Figure 4-25 Relationship between Height and Isotonic Endurance Limit
Figure 4-26 Relationship between Isotonic Endurance Limit and BMI
Figure 4-27 Relationship between Isotonic Endurance Limit and HGC
Figure 4-28 Relationship between Isotonic Endurance Limit and FAC
Figure 4-29 Relationship between Isotonic Endurance Limit and Trade for
Different Speeds and Dominancy
Figure 4-30 Relationship between Isotonic Endurance Limit and Smoking
Figure 4-31 Relationship between Isotonic Endurance Limit and Hand
Dominancy
Figure 4-32 General Neural Network Diagram
Figure 4-33 ANFIS Diagram
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CHAPTER ONE INTRODUCTION TO RESEARCH
In this chapter, research importance, motivation, significance investigations, relevance of
this research to ergonomics in aviation industry will be introduced,
outlines problem
statement. Since Muscle strength and muscular endurance considered as major
components and indicators of human body fitness and these are associated with health.
1.1 WORK RELATED MUSCULOSKELETAL DISORDERS
Work related musculoskeletal disorders (WMSD), introduced in different names in the
world
as
(Cumulative
Trauma
Disorder
(CTD),
Work-related
Disorders(WRULDs), Repetitive Strain Injury (RSI), Upper Limb
Upper
Limb
Disorder (ULD),
Occupational Cervicobrachial Disorder (OCD), Occupational Overuse Syndrome (OOS),
Musculoskeletal Disorder (MSD) in Great Britain, Canada, Australia, Holland, United
States, Japan, Scandinavia, Australia, New Zeeland and Holland. The most used name
worldwide is the Work-Related Musculoskeletal Disorder (WMSD), work related
musculoskeletal disorders, defined in different ways according to World Health
Organization (WHO, 1997) WMSDs are defined “as multi factorial where a number of
risk factors contribute significantly to their development and their risk factors are
classified as physical, work organizational, psychosocial, individual, or social-cultural”,
according to Canadian Centre for Occupational Health and Safety, Work Related
Musculoskeletal Disorders (WMSDs) defined as “a group of painful disorders exhibited
in body muscles, tendons, and nerves, WMSDs and associated muscular discomfort hurt
1
and pain, exhibited in muscles, tendons, and nerves”. According to the National Institute
of Occupational Safety and Health (NIOSH), WMSDs are “those diseases and injuries
that affect the musculoskeletal, peripheral nervous, and neurovascular systems that are
caused or aggravated by occupational exposure to ergonomic hazards”. According to the
WHO, they characterized “work-related” complaints as multi-factorial because of
surrounding work and multi-factorial nature, this enabled them from distinguishing the
risk factors that contributed to cause these diseases, these factors are individual
capabilities, physical limitations, work organizational policies, psychosocial, and sociocultural. According to the Canadian Centre for Occupational Health and Safety, these are
very difficult to characterize within the classification of traditional diseases. WMSDs
included repetitive motion and strain injuries, Cumulative trauma, Soft tissue, and
regional, occupational and overuse musculoskeletal disorders”. The NIOSH classified
and grouped WMSDs into four main groups based on distinct features:
1. Body parts, muscles, joints, nerves, and spinal cord injuries and discs.
2. Occasional disorders (events) such as fall or slips.
3. Intensity (intermittent/persistent), that based on historical body disorders
discovered in later medical checkups.
Special or distinctive disorders like (carpal tunnel syndrome), which is defined according
to the Canadian Centre for Occupational Health and Safety, as a common nerve
entrapment disorders that caused by long time intensity of work and or repetitive work,
according to Health and Safety Executive, they classified WMSDs based on risk factors
into four groups as follows: 1) Task-related factors, 2) Environment-related factors, 3)
Psychosocial factors, and 4) Worker-related factors. Other researchers like Fernandez and
2
Marley (2011) initiated the classification of WMSDs based upon the affected part of
human body,
upper extremities
like tendons disorders, Thoracic outlet syndrome,
Neurovascular disorders, Vibration syndrome, White finger syndrome, and Nerve
disorders), lower extremities diseases and low back pain that mainly caused by manual
material mishandling (MMM), like pulling, pushing, lifting, Hand-Arm vibration, etc.
Figure 1-1 and figure 1-2 provides examples of the work related musculoskeletal
disorders:
.
Figure 1-1 Carpal Tunnel Syndrome
3
Figure 1-2 Lateral Epicondylitis
As a summary, WMSDs resulted from abnormal conditions or human body physical
activities that included risk factors
posture, extended duration,
like doing a certain job repeatedly (repetition),
recovery time,
extra repetitive motions,
psychosocial
factors, excessive physical work, workload and pacing, extended use of human muscle,
hand-arm vibration,
cold stress,
uncomfortable awkward postures, force,
velocity/acceleration and mechanical stress, caused by or over a long period that exceeds
worker body limits. Some statistics were revealed about these injuries, according to the
United States Bureau of Labor Statistics (BLS, 2010) 40% injuries pertained to tears and
strains, with 36% pertains to back injuries, 26% pertains to lower body part extremities
and finally 12% for shoulders and hand injuries, where smallest portion pertained to
upper extremities (forearm and hand), as shown in Figure 1-3 (BLS, 2010).
4
Figure 1-3 Work Related Musculoskeletal Disorders, Non-Fatal (Centers for Disease
Control and Prevention (CDC, 2010)
Statistical research shows that WMSDs vary considerably from one job to another and
depend on gender. According to Jeong (2005), “they are widespread among the nursing
aides, attendants and healthcare workers such as sonographers” with higher rate in
females, followed by the freight, stock, and material movers workers. According to BLS
(2010), Figure 1-4 shows the number of Injuries due to WMSDs for particular
occupations, cost and risk associated with WMSDs.
5
Figure 1-4 MSDs Injuries and Illnesses (BLS, (2010)
Work related musculoskeletal disorders (WMSD), incurred industries high cost and in
most cases the precise cost is not known because of inaccurate estimates, since it includes
many costs, like workplace and medical costs. According to Davies and Teasdale (1994),
in Great Britain the overall cost of Work related musculoskeletal disorders (WMSD) that
includes (work-related illnesses beside avoidable accidents) “between £6 billion and £12
billion annually”. According to NIOSH (1997), WMSDs cost was around $13 billion in
the United States annually, while according to AFL-CIO (1997) had more estimate
exceeds $20 billion annually, overall and regardless of the assessment used, the problem
is large both in health and economic term (NIOSH,1997). However, David et al. (2008)
developed “Quick Exposure Check (QEC) for assessing exposure to risk factors for
work-related musculoskeletal disorders, which is an observational tool developed for
Occupational Safety and Health (OSH) practitioners to assess exposure to risks for work-
6
related musculoskeletal disorders and provide a basis for ergonomic interventions”, as
shown in Figure 1-5 (QEC Assessment Form).
Figure 1-5 QEC Assessment Form
7
1.2 HUMAN MUSCLE FATIGUE (DEFINITION and DESCRIPTION)
The prolonged, accumulated and repetitive job tasks can lead to adverse effects on the
human body parts and or muscles tissues like injuries and pain. Rohmert (1960, 1966)
defined human fatigue as a “periodic process in every living organism, and all organisms
are recoverable from fatigue by nature”, he also mentioned that fatigue can be recognized
by both the reduction in activities accompanied by feeling of fatigue. Edwards (1981)
defined fatigue as is “the failure to sustain the required job or task force, muscle fatigue
cause a reduction in the maximum voluntary contraction (MVC) and can be induced by
exercise. Fatigue can happen in both material, animals and human beings as response to
repeated or extra loads beyond their capabilities, material fatigue might lead to fracture of
the material, however, it is less harmful condition in humans but will reduce the strength
and performance of human body and mental awareness. Fatigue has a significant effect
on human performance. Snook and Irvine (1969) and Snook (1978) conducted
physiological and psychophysical fatigue experiments to measure the effect of fatigue on
performance, he stated that there is a significant relationship between performance and
psychological measures of fatigue and none consistent relationship between performance
and physiological measures of fatigue. Figure 1-6 shows median days away from work
and incidence rate due to injuries and illness by nature (BLS, 2010) and figure 1-7 shows
number of sprain, strain, and tear cases requiring days away from work by selected part
of body, industry (BLS, 2010).
8
Figure 1-6 Median Days Away From Work and Incidence Rate Due To Injuries and
Illness by Nature (BLS, 2010).
Figure 1-7 Number of Sprain, Strain, and Tear Cases Requiring Days Away From
Work by Selected Part of Body, Industry (BLS, 2010).
9
Human physical fatigue may be caused on both levels the main central nervous system
that drives the motoneurons and on the muscle level
peripheral changes. Researchers
classify human fatigue into two types, the Physical Fatigue and Mental Fatigue as
follows: 1) Physical or muscular fatigue happens when the human body muscles fails to
sustain and utilize any extra amount of loads and exert forces for defined job. Physical
muscle fatigue, also defined as the decline in the human muscle strength that lead to
reduction in ability to produce muscle force. According to Vollestad (1997) and Chaffin
et al. (1999), this type of fatigue resulted in reduction in the capacity or ability to exert
and generate any extra force to any new voluntary effort, this research will explore hand
grip limitations that lead to Physical fatigue; 2) Mental fatigue happens when the human
body attention or level of consciousness reduced for any reason, according to Baumeister
(2002)
“Mental Fatigue could lead to reduction in human memory, wrong or late
decision, causing sleeping problems, etc.”.
10
Ergonomics researchers classified and typed human muscle fatigue according to the
(motor pathways) connection means between brain and muscles as either central fatigue
or Supraspinal fatigue: 1) Central fatigue, where the body has a general feelings of
tiredness, weakness and exhaustion according to Taylor et al. (2005) the Central fatigue
defined as a “progressive exercise-induced reduction in voluntary activation or neural
drive to the muscle;” 2) Supraspinal Fatigue,
Where
a specific part of the body has
feelings of tiredness, weakness and exhaustion, recognized as Localized Muscle Fatigue
(LMF). The LMF caused a reduction in muscle strength and it’s a job time dependent.
Hainaut (1989) stated that the Localized Muscle Fatigue (LMF) happened when human
muscle cannot maintain the necessary force level due to decrease in the amount of
generated muscle tension. According to Taylor et al. (2005) supraspinal fatigue defined
as “an exercise-induced decline in force due to suboptimal output from the motor cortex”.
Blackwell et al. (1999) mentioned that the Localized Muscle Fatigue LMF is the
incapability of a muscle to keep the required job force. Edwards (1981) mentioned that
“maximum voluntary contraction (MVC) is graded according to tension generated
together with the number of fibers recruited, it can be attributed to failure of rate of
energy to meet the demand”. According to Gandevia (2001) “spinal and supraspinal
factors in human muscle fatigue, stated that MVC in most cases are less than the actual
maximal muscle force”. The human physical fatigue rate increased in heavy loads over
short time job tasks or small load over an extended period of time besides the repetitive
tasks and directly proportional with the amount of load force, load exertion time, and
abnormal postures and inversely proportional with rest time. According to Kumar and
Fagarasanu (2003) the great amount of forces do not necessarily be the primer cause of
11
muscle fibers injuries, and he emphasized that a repetitive low muscular force might
cause injuries to the human muscles. Also according to Sjogaard et al. (2000) a
continuous recruited muscles fibers because of an impairment in the local muscle
metabolism that become deleterious after repeating the same recruitment pattern. The
causes of physical fatigue in human or material depends on their specifications
capabilities and limitations together with many other factors like (task, environment,
psychosocial and Worker-related) factors that includes (doing a certain job repeatedly
(repetition),
posture,
extended
duration,
recovery time, extra repetitive motions,
psychosocial factors, excessive physical work, Workload and pacing, extended use of
human muscle, hand-arm vibration, cold stress, uncomfortable awkward postures, force,
velocity/acceleration and mechanical stress caused by or over a long period that exceeds
worker body limits. According to Chaffin et al. (1999), awkward postures dramatically
increase speed of fatigue occurrences, researchers also studied the posture effect like
Sjogaard et al. (2000) who found out that abnormal awkward postures cause higher
fatigue than normal neutral postures which cause lesser fatigue. According to BLS (2010),
Figure 1-8 shows the nonfatal injuries and median days away from work rates.
12
Figure 1-8 Average days away from Work due to Repetitive Motion in
Comparison to all other nonfatal Injuries (BLS, 2011)
From physical point view the muscle fatigue can be controlled through different means
through controlling the exerted force volume, job total repetitions, job
durations,
postures and rest periods, taking into considerations that the fatigue feelings start when
the above factors limit exceeds the human muscle limitations and capabilities, and these
factors have an effect on each other where working under normal postures still exposed to
physical fatigue in long periods jobs and increased rate of fatigue happened in the
awkward posture, all of the above factors also resulted in human muscles pain and body
parts complaints and disorders, and this is a very important factor where the job design
the job resting periods to decrease the muscle fatigue occupancies. Researchers and job
designers always look for the best reliable methods to measure the fatigue critical point
since it is different according to many factors like (task, environment, psychosocial and
worker-related factors besides the occurrence nature. Ergonomics fatigue experts usually
use the following approaches to find out the fatigue limitations: 1) the physiological
approach, where in this approach researchers measure the human body heart rate, oxygen
13
intake rate, and amount of energy expenditure. These measures help them in job and
different tasks design within acceptable limits. According to Dempsey (1998),
physiological approach responses used to insure that human body doing the jobs within
acceptable limits; 2) The psychophysical approach, in this approach depends on human
subject judgment and rating of stress and strain on their joints and muscles, some
researchers like Snook (1978) offer a surveying standard tool that can be used to measure
the psychophysical assessment. According to Snook (1978), the psychophysical approach
include the individual subjective rating to evaluate the fatigue of different body parts
muscles and joints; 3) The biomechanical approach, in this approach, according to
Jorgensen et al. (1999), the researchers use the mechanics principles to measure body
parts moments, against human physical structure, like torque, shear forces, compression
rate on (joint, spines), according to Jorgensen (1999), in the biomechanical approach the
mechanic principles used to evaluate fatigue limits through the measure of tensile, shear
and compression, moment and torques on body parts of the human body.
14
1.3 HUMAN GRIP STRENGTH
In order to study general body strength from all aspects, ergonomics researchers divided
the human body muscles strength into three type’s isometric, isotonic, and isokinetic,
when exposed to fatigue these muscles strength will be reduced: 1) Isometric Muscle
Strength: Chaffin (1975) defined the isometric muscle strength: as the “capacity to
produce torque or force by a maximal voluntary isometric muscular exertion”. Jackson
(1994) defined it as the “ability to exert maximum force without 10% of the body
strength as stated by Rohmert (1966); 2) Isotonic Muscle Strength: In the Isotonic Muscle
Strength the muscle length changed in none constant speed during movement of the body
parts. Knapik et al. (1983) defined the Isotonic Muscle Strength as the “capacity to
produce torque or force while the muscle changes length during contraction and cause
movement of the body part”. TeachPe Team (2012) classified isotonic muscle strength
into two types depending on the length of muscle: A) Eccentric Isotonic Muscle Strength,
where muscle length extended during the contraction; and B) Concentric Isotonic Muscle
Strength, where muscle length shortened during the contraction; 3) Isokinetic Muscle
Strength: In the Isokinetic Muscle Strength, the muscle changes its length in constant
rate/ manner, Jackson (1986) defined the Isokinetic Muscle Strength as the “the ability to
exert maximum force with producing movement”.
Hand grip is one of the first most used body parts, hand grip does not act by itself it is
related to hand muscles strength. According to Gonzalez et al. (1997) the hand forearm
and hand 35 different muscles working together to achieve the necessary movement, the
hand grip strength used as an indicator of the upper body general strength, and its
assessments found useful in evaluating the advancement of patients that are undertaking
15
physical therapy. According to Poitras (2011) hand grip strength can be used as a very
important screening tool in evaluating a human overall health, hence, he used in his
research the hand grip strength as reference indicator of the human muscle mass to find
out and predict future events such as "post-operative complications". Hand grip strength
readings helped nutritional experts and health practitioners in their jobs to prescribe and
design the body exercises, nutritional strategies and other interventions to improve the
human overall health and vitality. According to Stafford et al. (1989) the hand grip
strength measurements, especially the maximum grip strength are used by many
researchers to use different body measurements, hand and hand grip used in many human
activities and sports, can be used in altered postures to accommodate the task nature.
According to Koley et al. (2009) grip strength defined as the “force applied by the hand
to pull on or suspend from objects and is a specific part of hand strength”.
According to researchers there are two types of hand grips, defined as its purposes the
Pinch and crush grip, where different hand muscles used for gripping purposes where
their number depends or grip use either (needs partial or
maximum power grip).
Bookfield (2008) classified grip strength into both Crush and pinch grip as follows: 1)
Crush Grip, It is the same as to grip power, just like handshaking situation where the
hand palm is touched by the four fingers of the hand, this position resembles the strongest
grip for the hand, 2) Pinch Grip, this situation happened in precise griping accurate
situations, when object is held by two or three fingers of the hand (like surgeons and high
tech workers), pinch grip is used to exert and get maximum possible force and 3) Support
Grip, where we use external handle to grip /catch an object, some researchers and
employers used, hand grip strength can be used as strength indicator. According to
16
Boissy et al. (1999), grip strength used as an indicator for overall health and physical
strength. At present, increased employers and organizations use the hand grip tests and
strength as pre-hiring screening measure and as a worker performance indicator (e.g., the
police, the army, fighter pilots, Special Forces and fire departments, etc.). According to
Ruiz-Ruiz et al. (2002) recruiters realized that the hand grip strength is one of the
essential requirements for job applicants that needs physical strengths to pass before
getting their job, like industries included jobs that includes assembly, holding, repairing,
packing, processes, etc. Dubrowski and Carnahan (2004) mentioned that during industry
lifespan the hand grip strength may be used as a labor performance measurement.
According to Bohannon (2004), health experts may use the maximum grip strength as an
upper-limb strength suitable indicator. According to Wind et al. (2009), maximum grip
strength can be used as children and young adults general muscle strength.
1.4 Hand GRIP STRENGTH TEST
There are many muscles used during the power hand grip strength test as follows by
Carlson (1970): 1) The flexor muscles of the arm and 2) The extensors muscles of the
arm. Vanish (2012) stated that both the flexor muscles and the extensors of the arm are
used for grip strength, and to stabilize the wrist, hand grip strength is a result of the hand
ten main muscles as follows: 1) Forearm muscles, 2) Flexor Digitorum Profundus, 3)
Flexor Pollicis Longus, and 4) Flexor Digitorum Superficialis. Finally other muscles
where these muscles that help to make grip according to Gonzalez et al. (1997) such as:
1) Flexor Digitorum, 2) Superficialis, 3) Flexor Carpi Ulnaris, 4) Flexor Carpi Radialis,
and 5) Abductor pollicis. Figure 1-9 show the muscles involved in grip strength
(Vansuh, 2012).
17
Figure 1-9 Muscles Involved in Grip Strength (Vansuh, 2012)
1.4 MAXIMUM VOLUNTARY CONTRACTION (MVC)
Segen (2002) defined MVC force as the static measurement of strength which is the
same as the maximum force achieved in one single voluntary effort. According to Tufts’
University Nutrition Collaborative Center (2003), the MVC force is defined, in more
depth, as the power grip force resulted of “forceful flexion of all finger joints” associated
with maximum voluntary force (MVC) that can be achieved under standard bio kinetic
conditions, study revealed by (Brenner et al., 1989; Luna-Heredia et al., 2005) dominant
grip strength increased with age and was greatest for the (35 to 44) year old cohort.
Massy-Westropp et al. (2004) new study performed by Concordia University at the
McGill Nutrition and Performance Laboratory on 203 patients with advanced-stage
cancers finds important relationship between individual’s handgrip strength and cancer
rates survival. The researchers find that simple person handshake (simple squeeze) can
reveal a lot of information about an individual's attitude and character, stated that besides
18
using it as a “diagnostic tool to gauge strength and quality of life among critical patients”
and measure the individuals capability, ability to battle the deadly disease. New research
studies about physical activity effect on middle-age in Boston Medical Center, discussed
through
American Academy of Neurology's" annual meeting (2015), found relation
between hand grip strength and walking speed for 2,400 people during 11 years, results
found that “ a slower walking speed in middle age were one-and-a-half times more likely
to develop dementia compared to people with faster walking speed and people with a
stronger hand grip was associated with a 42 percent lower risk of stroke in people over
age 65 This may assist the physicians to determine risk of developing dementia or stroke
for middle-aged people”. According to Sirajudeen et al. (2012), in a study on a total of 50
Indian male population Jamar dynamometer, they found Positive correlation between the
males physical factors like (body mass index, weight, height, anthropometric
measurements) and grip strength. They stated that the grip strength assessment results
considered and accepted as good indicator of “nutritional status, bone mineral content,
muscular strength and functional integrity of upper extremity”, they also have a strong
role to measure treatment strategies results of hand. Mitsionis et al. (2009) conducted a
study using data from the Health and Retirement Survey (HRS), they studied age and
education regressions. They found that “hand-grip strength to produce an easily
interpretable, physical-based measure that allows us to compare characteristic-based ages
across educational subgroups in the United States”, also “a strong handshake can indicate
power, confidence, health, or aggression, the strength of a person’s grip may also be a
useful way to measure true age”. They found that the hand-grip strength testing results be
used as dependable predictor measurement of the human population aging “future
19
mortality, morbidity, cognitive decline and the ability to recover from hospital stays.
Their detailed findings was as follows “The hand-grip strength of 65 year old white
males with less education was the equivalent to that of 69.6 (68.2-70.9) year old white
men with more education, indicating that the more educated men had aged more slowly”.
According to Swift et al. (2012), research objective was to “to assess how age-related
social comparisons, which are likely to arise inadvertently or deliberately during
assessments, may affect older people's performance on tests that are used to assess their
needs and capability”. Using participants from UK centers and senior's lunches in the
South of England, they establish the normal hand grip strength values data and check
relations with the anthropometric factors, by testing 232 participants using the Jamar
dynamometer. They found the following “ Right hand and dominant hand GS were found
to be higher and statistically significant compared to left hand and non-dominant hand
GS, respectively. Men had higher values of GS compared to women, negative association
was observed between age and dominant hand GS, positive association was documented
between height and dominant hand GS, while the respective comparison for weight and
dominant hand GS documented a statistically significant positive association only in the
male group. Positive association between BMI and dominant hand GS was seen in female
individuals. Additional factors associated with GS should be the goal of future
investigations”, as a conclusions they found that “Due to the potential for age
comparisons and negative stereotype activation during assessment of older people, such
assessments may underestimate physical capability by up to 50%, because age
comparisons are endemic, this means that assessment tests may sometimes seriously
underestimate older people's capacity and prognosis, which has implications for the way
20
healthcare professionals treat them in terms of autonomy and dependency”, the key
messages of the Mitsionis et al. (2009) study as follows:
1. “Psychosocial factors may influence how strongly physical effects of ageing manifest
themselves.
2. Age comparison creates a stereotype threat, which can reduce older people's hand
grip strength by up to 50%, Healthcare professionals should be aware of the potential
for age comparison and stereotypes to affect outcomes of assessments of older
people.
3. Hand grip is an ‘objective measure’ of physical capability among older people. It is
predictive of frailty, morbidity, disability and mortality.
4. This research was conducted in a non-medical setting and involved participants in
good health with a small convenience sample. However, the effects remain significant
even when age, gender, education, degree of arthritis in the hands, type of residence
and location of testing.
5. Further research is needed to evaluate the prevalence of age comparisons in clinical
testing settings, and effects on people of different ages.
21
6. Other studies about assessment of muscle status in chronic kidney disease patients
using hand grip strength (HGS) tool and body composition monitor (BCM) in Cairo
University”.
In summary, WMSDs are great in mostly all industries where the job tasks are worked by
hands. Muscle strength is classified by three types according to movement type as
isometric, isotonic, and isokinetic, physical fatigue is can happened for many reasons like
overloads, extended times, abnormal postures and rest periods, researchers muscle fatigue
assessed through three approaches Psychophysical, Physiological, and Biomechanical.
The best approach found in such cases is the psychophysical approach where fatigue is
assessed subjectively by subject individuals, which is used in the current research.
22
1.5 PROBLEM STATEMENT
Human muscle fatigue is one of the most researched subjects in ergonomics, improper
human designed job will lead to increased human muscle fatigue that results in high rate
of WMSDs, which incur the industry and organizations a lot of worker compensation
money for their injuries. Many researchers stated that the human muscle fatigue research
are complex in nature, hand grip strength are extremely important factor that have a great
effect on the overhaul human body performance in terms of both the volume of force
exerted and fatigue (endurance limit). Hand grip also can be used as an indicator of
human general health and related to many diseases. Each type of the muscle strength has
its specific use. In Isometric the muscle strength, muscle is used for holding static force.
But in isotonic and isokinetic the muscle strength take place to adjust the dynamic load,
all types of work includes static and dynamic combinations, so this project research will
do both types of forces besides other independent factors like (hand grip circumference,
trade,
BMI, holding time, etc.). In calculating the fatigue limits (expressed as
Time/Cycles to Fatigue) for specific aviation, retired and active duty air force and current
technicians/engineers from Jordan, ergonomics researchers used different approaches to
find out the fatigue limits and nature like biomechanical approach in human muscle
fatigue modeling torque and joints stress and or the physiological approach human
muscle fatigue modeling and the rest used psychophysical approach which has a key role
in WMSDs. This research will use the psychophysical approach because of its high
reliability than both biomechanical and physiological approaches, and same decision is
suggested by available literature.
23
1.6 RESEARCH OBJECTIVES
A lot of research has been performed to measure the MVC force affected by different
independent factors; however, most of them they resulted in specific (one or two factors
effect) and not a comprehensive model including all parameters to predict the MVC force
either for complete or submaximal of maximum voluntary and fatigue limit t for different
parts of the body (arm, leg, and shoulder). Another issue is that limited literature
available that develops isotonic muscle strength models to predict the fatigue limit, on the
contrary a lot of researchers studied the fatigue limit for isokinetic muscle strength. Also
found that the biomechanical and physiological approaches has less accuracy and lead to
less understanding of fatigue effect. Literature available for last 50 years has different
outputs and point views regarding the effects of many independent variables, besides
using amore precision dynamometer, also many researchers used ANOVA for their
analysis, and very limited used the neural network and fuzzy logic modeling. ANFIS
approach that provides more precise outputs, based on the findings, recommendations
will be made for the applications in appropriate domains. There is a very few researches
about muscle strength, fatigue limits and investigations in the area of hand grip strength
and endurance in aviation trades and especially for those that most of them are smokers
and have an older ages from Jordan. The purpose of the research the primary goal is to
use the psychophysical approach to investigate the hand grip strength MVC and,
endurance fatigue limits in the area of:
A. Aviation trades.
B. Smoker’s aviation trades.
C. Older ages subjects.
24
D. Jordanian Subjects.
E. Include high precision apparatus (Digital dynamometer).
F. Include new factors like forearm, postures, right and left hand.
Investigate and find out the correlations among the different factors of, BMI, hand grip
circumference, resting heart rate, holding time and postures (standing and seating),
submaximal of maximum voluntary contraction or MVC and the number of cycles/time
before the human arm muscle gets fatigued as reported by the aviation individual and his
perception of pain, build models will be built to predict the MVC force which will take
into consideration all independent factors, build prediction fatigue model for my subjects
that involves static force and dynamic force, design another set of models that use both
(isometric and isotonic muscle strengths) to find out the effect of independent variables
on the maximum endurance limit for static force and frequency of gripping for
submaximal isometric muscle fatigue limit (endurance limit). Using all independent
factors, models will be designed accordingly to use the Mathematical and Artificial
neural network and ANFIS fuzzy inference system. Independent Variables and their
levels/notations are shown in Tables 1-1 for the experiment.
Table 1-1 Independent Variables
Dependent Variables
Independent
Variables
1- MVC
2- Isometric Endurance
Limit (20%, 40%, 60%,
80%)
3- Isotonic Endurance
limit (20-60%)
Age (years)
25
Treatment Levels
1)
2)
3)
4)
5)
6)
A0: (25-<30)
A1: (30-<35)
A2: (35-<40)
A3: (40-<45)
A4: (45-<50)
A5: Above 50
Fixed Factors
Trade
1) APG: Airplane General
2) E&I: Electrical and
Instrument
3) COMNAV: Communication
& Navigation
4) Eng: Engine
5) GSE: Ground Support
Equipment
Smoking
1) Smokers
2) Non-smokers
Body Mass Index
(BMI)
1) Small: S (19-<25)
2) Medium: M (25-<30)
3) Large: L above =>30
Hand Grip
Circumference (CM)
1) Small: S (=< 21.5)
2) Medium: M (>21.5 -23.5)
3) Large: above 23.5
Hand Dominancy
1) D: Dominant
2) ND: Non-Dominant
Forearm
Circumference (CM)
1) Small: S (<= 27.5)
2) Medium: M (>27.5-31)
3) Large: (above 31)
Posture
1) Sitting: SIT
2) Standing: STD
Height (M)
1) Short: S (<= 1.70)
2) Medium: M (>1.70-1.81)
3) Tall: T (above 1.81)
1- Jordanian Subjects
2- Digital Dynamometer
26
1.7 RESEARCH SIGNIFICANCE
After the detailed survey of literature, the following observations were made:
1. Limited formal investigation on the effect of the combination of isometric and
isotonic endurance on fatigue has been conducted. Since all workers use the
combination of both of isometric and isotonic forces.
2. Include new factors like (aviation trades, males with more smokers, older ages,
Jordanian subjects, digital dynamometer, standing posture, etc.) besides traditional
researched parameters like gender, BMI, hand grip circumference, resting heart rate,
holding time for isometric forces followed by isotonic contractions and the use of
gloves.
3. Build mathematical models for those independent variables. The current research
will:
A. Assist aviation industry in identifying the influential factors on the human
performance of the jobs that involve the use of hand muscles.
B. Give better understanding about muscle strength in large smoker’s subjects.
C. Give better precision for MVC values by using digital dynamometer.
D. Consider new factors like hand volume and forearm circumference
E. Find relation between different types of sickness and grip strength.
F. Give better idea about race grip strength.
G. Build more precise models like neural network-based and fuzzy logic-based
models.
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1.8 DISSERTATION ORGANIZATION
The present dissertation is a part of a major ongoing research effort to study the different
factors affecting the maximum and partially hand grip strength and belonging factors that
affecting the both isometric and isotonic muscle fatigue, especially in presence of new
parameters that were not studied before. The final goal will be to develop MVC and
fatigue models that can be used to find out the maximum endurance period for isometric
muscle strength and number of cycles for isotonic muscle strength for the all new
parameters. This dissertation is organized into the five chapters as follows: Chapter one
provides a general introduction to human muscles researches their importance and
describes the concepts and meanings of the terms used in the research. Chapter two
introduce very detailed
literature review and surveys to explore the information about
(hand grip strength, MVC, isometric muscle strength; isotonic muscle strength, muscle
fatigue, and endurance limit modeling and would be useful for the work. Chapter three
introduce and explain the methodology of the performed experiment including,
instruments and variables used. Chapter four explains and discusses the mathematical and
soft computing models and Chapter five outlines summary of results, their possible
application in aviation industry, and ideas for future research.
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CHAPTER TWO LITERATURE REVIEW
This chapter introduces massive literature on this research in addition to new studies
relevant to the research, the available studies on maximum voluntary control (MVC)
force, isotonic, isometric and isokinetic muscle fatigue limits are discussed, investigated,
reviewed, and classified according to factors which affected the main research. The
significance of the dissertation topic will be elaborated upon and then, in the following
paragraphs, the hand grip strength studies and related subjects will be reviewed.
2.1 MAXIMUM VOLUNTARY CONTRACTION (MVC DEFINITION)
Several authors conducted many research studies to evaluate the effect of different factors
on MVC force. According to Segen (2002) the MVC force is a static human muscle
strength measure and indicates the maximum force that can be achieved in one single
voluntary effort,
Kamimura and Ikuta (2001) researched the relation of maximum
isometric contractions and endurance limits, their research resulted in a strength-time
curve relationship between maximum strength and length-time. It had an early peak
followed by gradual decrease in strength.
According to Stulen and De Luca (1981), the maximum voluntary contraction (MVC)
value depends on both of the muscles strength and brain related factors. This is where the
human muscle strength are influenced by different factors like age, skeletal structure,
length and volume of muscles and exercise. According to Stulen and De Luca (1981),
the MVC exerted by two mechanisms, the motor firing frequency and recruitment of that
29
motor, is where the firing frequency initiated by a single motor unit that fires the muscle
fiber. According to De Luca (1985), the relation between the maximum contraction force,
the firing frequency, and number of recruited motor units are directly proportional. Al
Zaman et al. (2007) stated that the smaller motor units are recruited earlier than large
motor units, when human muscle starts to produce force and their firing frequencies start
at higher levels and this is matched by the rule of size principle. Sorensen et al. (2009)
stated that manual tasks workers who used hand static load more frequently, get more
chances in facing muscular disorder complaints, especially the carpel tunnel syndrome,
and he suggested that job design should include the human ergonomics principles,
capabilities and limitations.
Smoking Effect on MVC were researched by many scientists, Asano and Branemark
(1970) mentioned that most of researchers found, and from a medical point view, that
smoking lead to
profound vasoconstriction which will develop a microcirculation
complete block that results in tissues starving of nutritive blood and bypass from
arterioles to venues. Isaac and Rand (1969) also mentioned another effect of smoking
where after an average of 30 minutes after smoking, nicotine levels increase in plasma up
to 10 micrograms per 100 mm of blood. Sorensen et al. (2009) mentioned that a smoker
worker’s capabilities decreased because of lung incapacity to provide more oxygen to
muscles. Davis (1960) also mentioned that nonsmokers can exert more force because the
non-smokers cardiovascular system is greatly affected by smoking residue in the body,
where the heart rate (pulse) increased dramatically with each cigarette for an average of
21 beats (pulses) per minute.
30
Hand dominancy effect on MVC were researched by many scientists, Sorensen et al.
(2009) mentioned that a worker’s capabilities are affected by hand dominancy, where
dominant hand can get more MVC. Incel et al. (2002) studied the grip type’s effect (grip
and pinch strength). Their research resulted in favor of the dominant hand. Armstrong
and Oldham (1999) studied the effect of dominancy on hand grip strength between the
non-dominant and dominant hand. For right-handed and left-handed subjects, he found
that that there is important strength difference found in (0.1–3%) in right-handed people
and no worthy difference found for dominancy issues in left-handed people. Ibarra-Majia
et al. (2012) searched the effect of standing and sitting posture on hand and pinch grip
strength. They found that subjects exerted more grip strength for the dominant hand by
3.9%, and for pinch grip, no statistical difference for dominant or non-dominant positions.
Bohannon et al. (2006) searched the left and right hand grip strength, they found that the
dominant right hand is stronger than the monodominant left hand. Koley et al. (2009)
found that the dominant hand had higher grip strength than the non-dominant hand for all
subjects. He introduced the‘‘10% rule’’ and suggested that dominant grip strength is
about 10% greater than the non-dominant grip strength. Petersen et al. (1989) verified
the‘‘10% rule’’ and found that it is applicable only on right hand dominant subjects only.
Left and right hand people effect on MVC were researched by many scientists, Incel et al.
(2002) studied the grip types effect (grip and pinch strength) with left and right hand
people and found that no difference in grip strength between left and right handed
persons. Günther et al. (2008) studied the maximum hand grip strength and found that an
average of hand grip strength was in right: 49 kg; left: 47 kg for males, and right: 29 kg;
left: 27 kg for females.
Right hand exerted much more strength than left hand.
31
Armstrong and Oldham (1999) found that there are important strength difference around
(0.1–3%) in right-handed people and no worthy difference found for dominancy issues in
left-handed people. Bohannon et al. (2006) searched the left and right hand grip strength
issue. They found that the right hand is stronger than the left hand. Koley et al. (2009)
found that where he found that sedentary females, equally right and left hand exerts
higher force power than laborers. The study revealed that “dominant grip strength
increased with age and was greatest for the 35 to 44 year old cohort”.
Endurance Limits effect on MVC were researched by many scientists, according to
Miller et al. (1993) endurance is a term that is used to indicate physical fatigue point,
which generally refers to the total time before fatigue state happens. More specifically, it
is according to him "ability to perform prolonged muscular work at predetermined
intensity without external signs of fatigue", Kamimura and Ikuta (2001) conducted an
“evaluation of grip strength with a sustained maximal isometric contraction for 6 and 10
Seconds”, he researched the relation maximum isometric contractions and endurance
limits where they assessed the maximum grip strength. Their research resulted in a
strength-time curve relationship between (maximum strength and strength-time) that has
an early peak followed by gradual decrease in strength.
Different experiment apparatuses used by many scientists, Kamimura and Ikuta (2001)
used the dextral tooling in researching the relation maximum isometric contractions and
endurance limits. Bohannon et al. (2006) searched the left and right hand grip strength
issue on a total of 739 old subjects using the Jamar dynamometer. Kamimura and Ikuta
(2001) researched the relation maximum isometric contractions and endurance limits.
Their test intervals were limited to 6 and 10 seconds.
32
The gender effects on MVC were researched by many scientists, in most researchers the
male can exert more MVC than females, since that the female body and muscles structure
is different from the male body muscles. This result is expected especially when doing
the manual tasks. Sorensen et al. (2009) mentioned that in a worker’s capabilities, males
have more MVC than females. Incel et al. (2002) studied the grip types effect (grip and
pinch strength) on total of 149 male subjects (21 left-handed and 128 right-handed)
volunteers. Günther et al. (2008) studied the maximum hand grip strength, result data as
follows: average of hand grip strength in (right 49 kg; left 47 kg) for males and (right 29
kg; left 27 kg) for females, around 41% lesser than males. Montes (2001) investigated the
muscle volume effect on 38 subjects (24 males and 14 females). Ibarra-Majia et al. (2012)
searched the effect standing and sitting posture effect on both (hand and pinch) grip
strength, he used total of 44 subjects, (30 males and 14 females). Koley et al. (2009)
found the grip strength value for 200 middle aged (18-40) years old female subjects.
33
Table 2-1 Grip Strength Value for 200 Middle Aged Female Subjects.
Right Hand Strength
Left Hand Strength
(22.75
kg)
(23.63 Kg)
Sedentary
Laborers
(21.03 Kg)
(19.73 Kg)
Miller et al. (1993) used biological approach and by using 6 subjects from males and
females, he searched relationship between muscle characteristics and the strength. He
found that: A) Males are stronger than females, females got 52-66% of male strength and
B) Males are stronger because of muscle fibers size and distribution where females have
less lean tissue in the upper body.
Samples size and aging effects on MVC were researched by many scientists, Kamimura
and Ikuta (2001) research included (50 young subjects of ages 18-26, 25 males and 25
females). Incel et al. (2002) studied the grip types effect for total of 149 subjects (21 lefthanded and 128 right-handed) volunteers. Montes (2001) investigated the muscle volume
effect on grip strength for young subjects (21.87 years) for a total of 38 subjects (24
Males and 14 females). Ibarra-Majia et al. (2012) searched the effect standing and sitting
posture effect on hand and pinch grip strength, on total of 44 subjects, young aged
between (18 to 35) years old (30 males and 14 females). Bohannon et al. (2006) searched
the left and right hand grip strength issue on total of 739 old subjects classified in 4 old
age groups into 75-79, 80-84, 85-89, and 90-99 years. Koley et al. (2009) did a special
study on 200 middle aged (18-40 years) female subjects. Chatterjee and Choudhuri (1991)
found that that highest exerted MVC was for young subjects ages between (18-22 years
old). Petrofsky and Linda (1975) studied the aging effect on males isometric muscle
strength, for endurance limit of 40% of maximal strength and the heart rate and blood
34
pressure used very wide range of ages between (22-60) years old, 100 subjects. Most of
researchers like Asmussen and Heeboll-Nielsen (1955, 1956, and 1962) and Chatterjee
and Chodhuri (1991) obtained results and agreed that max strength can be achieved at
around age 20 as a peak amount then started to decline with older ages. Study revealed
that “dominant grip strength increased with age and was greatest for the 35 to 44 year old
cohort”.
Forearm circumference effects on MVC were researched by many scientists. According
to Fraser et al. (1999), “forearm circumference generally decreased with age for both men
and women, although this decline was less marked for women”. also, “sitting and
standing found that British subjects have slightly greater values for dominant forearm
circumference measurements in both men and women (29.1 cm vs 24.3 cm for men and
25.6 cm Vs 20.4 cm for women)”. Crosby and Wehbe (1994) found that “forearm
circumference delivered the best practical method for assessing the MVC grip strength”,
and muscle mass for both genders. Crosby and Wehbe (1994) showed that using second
handle position of the Jamar dynamometer was adopted for standardized assessment
position produces maximum grip strength measurements for most subjects.
Maximum grip strength effect on MVC were researched by many scientists Kamimura
and Ikuta (2001) obtained strength-time curve relationship between (maximum strength
and strength-time), that has an early peak followed by gradual decrease in strength. When
they researched the relation maximum isometric contractions and endurance limits,
Günther et al. (2008) studied the maximum human hand grip strength, the a average of
hand grip strength (right 49 kg; left 47 kg) for males and (right 29 kg; left 27 kg) for
females, around 41% lesser than males. Bohannon et al. (2006) found that to grip strength
35
inversely proportional with factor aging. Koley et al. (2009) found the following grip
strength values for 200 middle aged (18-40) years old females as shown in Table 2.1.
Chatterjee and Choudhuri (1991) found that the highest exerted MVC was for young
subjects’ ages between (18-22 years old). His study revealed that “dominant grip strength
increased with age and was greatest for the 35 to 44 year old cohort”. Massy-Westropp et
al. (2004) studied the height effect and found high effect as predictive for MVC grip
strength on MVC. Fraser et al. (1999) and Crosby and Wehbe (1994) stated that there is
positive correlation between physical factors and MVC, Grip type (grip and pinch
strength) was studied by many scientists, such as Incel et al., (2002). Their research
resulted in favor of the dominant hand and no difference in grip strength between both
person hands. Ibarra-Majia et al. (2012) searched the effect standing and sitting posture
effect on hand and pinch grip strength. They found that subjects exerted more grip
strength in standing position than sitting position by 3%, and for pinch grip no statistical
difference between standing and sitting positions but key pinch strength marginally
higher for standing and sitting positions, muscle volume. Montes (2001) investigated the
muscle volume effect on grip strength for young subjects (21.87) years for total of, 38
subjects (24 Males and 14 females). He used ultrasonography method to measure the
muscle sectional diameter for both (maximum voluntary isometric contraction position
and relaxation position). Their findings are in the Table 2-1 where higher muscle
diameters noted in the maximum voluntary isometric contraction position. Sherif et al.
(2012) found a positive correlation between higher body physical factors (forearm
anthropometric BMI, and hand muscle) with hand grip strength.
Posture (sitting and standing) effect on MVC were researched by many scientists. Ibarra36
Majia et al. (2012) searched the effect standing and sitting posture effect on hand and
pinch grip strength, they found that subjects exerted more grip strength in standing
position than sitting position by 3%, beside that the dominant hand exerted more grip
strength by 3.9% and key pinch strength marginally higher for standing and sitting
positions as shown in Table 2-2.
Table 2-2 Maximum voluntary for Standing and Sitting and Dominant Hand
Test
Standing and Sitting
Dominant Hand
Grip
Strength
Exerted more grip strength in standing
position than sitting position by 3%
The dominant hand exerted
more grip strength by 3.9%.
Pinch Grip
No statistical difference between
standing and sitting positions
No statistical difference for
dominant or non-dominant
positions
Right and
Left hand
Key pinch strength marginally higher for standing and sitting positions
Race effect on MVC were researched by many scientists, such as Brenner et al. (1989)
and LunaHeredia et al. (2005) who stated that “the population of South East Scotland
follow previously identified patterns relating to age and sex for other populations”.
Brenner et al. (1989) and LunaHeredia et al. (2005) found that “Spanish population mean
dominant grip strengths of 39.95 kg for men and 25.72 kg for women”. Crosby and
Wehbe (1994) stated that “the United States population in which the mean dominant grip
strength was 137 lb. (62 kg) for men and 81 lb., (37 kg) for women”. Incel et al. (2002)
tested Singaporean population and “get dominant grip strength of 86.06 (24.71) kg” and a
mean non-dominant grip strength of 79.13 (23.68) kg. Koley et al. (2009) did a special
study on 200 middle aged (18-40) years old female subjects of hand grip strength laborers
in India Punjab (Jalandhar). Sherif et al. (2012) performed a study in Indian males and
37
found a positive correlation between higher body physical factors (forearm
anthropometric BMI, and hand muscle) with hand grip strength in Indian males.
Heart rate effect on MVC were researched by many scientists. For example, Wilmore et
al. (2005), Rowell (1993) found that the untrained persons have higher heart rate other
than trained athletes. Sherif et al. (2012) performed a study in Indian males and found a
positive correlation between higher body physical factors (forearm anthropometric BMI,
and hand muscle) and both hand grip strength in Indian subjects. Finally, grip strength
models on MVC were researched by many scientists, Chatterjee and Choudhuri (1991)
searched the grip strength from the following factors (height, weight, age, body surface
areas for both left hands and right hands), and got maximum grip strength regression
model where correlation was positive for all factors and maximum grip strength, beside
that highest exerted MVC was for young subjects ages between (18-22) years old. Table
2-3 shows all experiment results.
38
Table 2-3 MVC Regression Models for MVC
Most of researchers like Asmussen and Heeboll-Nielsen (1955, 1956, and 1962) and
Chatterjee and Chodhuri (1991) identified linear relation for MVC and ages age 20 as a
peak amount then started to decline with older ages as shown in Figure 2-1.
Figure 2-1 Males MVC with Age
39
2.2 ISOMETRIC ENDURANCE LIMIT
Endurance limit has many definitions all of them refer to the human muscle’s capability
and ability of keeping and maintaining a predefined level of force (MVC %) over work
time, thereby making it a force-time relationship. Endurance limit is also defined as force
and time relationship, where the muscles capability and ability to sustain the whole or
percentage amount level of maximum voluntary MVC (Force) over time frame. Yeung
(1998) stated that isometric muscle strength cannot be considered as a good predictor or
indicator of general body health or strength. Mital and Faard, 1990, also has a similar
point of view as Soylu and Arpinar-Aysar (2010) Koley, Kaur and Sandhu (2009) that
isometric muscle strength cannot be considered as a good predictor because of the
absence of body movement and segment throughout maximal voluntary contraction
(MVC).
Endurance time types were classified by researchers, Al Zaman et al. (2007) ergonomics
scientists used the fatigability level limits and physiological characteristics to classify the
motor units into three types as follows: 1) Greatest resistant to fatigue happened in the
type I (S) Motor Units, 2) The average fatigue resistance, the type Ilia (FR) Motor Units
and 3) The weakest (defenseless to fatigue) the type IIb (FF) Motor Units. Biological
Studies were performed by researchers, Yeung and Evans (1998), who made a biological
study on 5 male subject’s fernoris muscle for different isometric voluntary contraction
levels by finding out the relationships of “the vibromyographic (VMG) and the
electromyographic (EMG) signals. A relationship was linear between the frequency
domain (MF) and time domain (RMS). Kaplanis et al. (2009) other biological study
where he measured Biceps Brachii (BB) muscle with 13 different parameters, by their
frequency, time and bispectrum domain, for different isometric voluntary contraction
40
levels (IVC) by calculating the surface electromyography (SEMG) values. He found that
the linear relationship, between the maximum amplitude increases and bispectrum muscle
parameter for all values except the condition of 30% - 50% of maximum IVC. Soylu and
Arpinar-Avsar (2010), searched biologically the fatigue and MVC relationships on 12
subjects biceps brachii muscles (BBM), by using the surface electromyography (SEMG)
signals. He found that minor increase in the force with biceps brachii muscles (BBM) can
reach the maximum (MVC) within two seconds only.
Sample subjects and aging effect on isometric endurance limit studied by researchers,
Yeung and Evans (1998) made a biological study on 5 male subjects’ fernoris muscle.
Garg et al. (2002) studied the relationship between endurance limits and different MVC%
from elbow flexion angles for 12 females. Soylu and Arpinar-Avsar (2010) searched
biologically the fatigue and MVC relationships on 12 subjects. Chatterjee and Chowdhuri
(1991) searched the MVC force, 40% of MVC, all age groups, and right hand
(dominancy) relationship. He found that no relationship between endurance values limit
and age. Chatterjee and Chowdhuri (1991) searched the fraction MVC at 40% level
where they found: 1) 40% MVC level independent of gender and age and 2) Dominant
hand sustained extra endurance limit average of 16 seconds more than the non- dominant
hand. Miller et al. (1993) used biological approach and by using 16 subjects from males
and females, he researched a relationship between muscle characteristics and the strength.
He found that males are stronger than females. Females got 52-66% of male strength. In
addition, males are stronger because of muscle fibers size and distribution and females
have less lean tissue in the upper body. Endurance and postures effect on isometric
endurance limit studied by researchers, Mogk and Keir (2003) measured the forearm
41
fatigue response for forearm posture and wrist combinations as shown in Table 2-4:
Table 2-4 MVC Fractions with Wrist Postures Effect
MVC Fractions
Wrist Postures
(5%, 50%, 70%, 80%, (neutral,
extended)
and 100%)
flexed,
Three Forearm Postures
and (pronated,
supinated)
neutral,
and
Results were as follows: 1) Wrist postures flexed affected grip force with different
forearm posture, 2 other wrist postures got altered muscle contributions and 3) Wrist
postures flexion got the highest muscle activation. Haque and Khan (2009) researched the
relationship between maximum voluntary contractions with different postures (ulnar
deviation of the wrist), he found that: 1) Wrist neutral position got the highest MVC, 2)
The MVC values getting higher when lower ulnar deviation and increase ulnar deviation
and 3) O’Driscoll et al. (1992) got different result where it was mentioned that the selfselected position resulted in getting the highest MVC. Haque and Khan (2009, searched
the wrist posture effect and found that the best posture and most comfortable is that
where the wrist posture was closer to the neutral position. Figure 2-2 show various hand
wrist posture
42
Figure 2-2 Various Hand Wrist Postures Used (Khan, 2010)
Endurance relationships and models found by researchers, Yeung and Evans (1998) made
a biological study on 5 male subjects’ fernoris muscle for different isometric voluntary
contraction levels by finding out the relationships of “the vibromyographic (VMG) and
the electromyographic (EMG) signals. He found that the relationship was linear between
the frequency domain (MF) and time domain (RMS). Kaplanis et al. (2009) did another
biological study where he measured Biceps Brachii (BB) muscle with 13 different
parameters: their frequency, time and bi spectrum domain, for different isometric
voluntary contraction levels. By calculating the surface electromyography (SEMG)
values, he found that linear relationship exists between the maximum amplitude increases
and bi spectrum muscle parameter for all values except the (30-50%) envelop of
maximum MVC. Garg et al. (2002) searched the fraction of MVC and endurance level
for different (elbow flexion angles), he found that from (15% to 30%) MVC, the
endurance limit decreasing in high rate and from (30% - 90%) MVC the endurance limit
decreasing in slower rate increased rapidly, as result there is Nonlinear relationship
between MVC force and time, Minnal (2014) found that the curve is never asymptotic
and inversely proportional between MVC and endurance limit for certain elbow flexion
angle (increase the fraction MVC resulted in reduce endurance limit). Al Meanazel
(2013) found out that the maximum endurance limit results for nonsmoker male that have
both higher BMI and higher grip circumference when using the dominant hand.
Endurance level at different MVC% effect on isometric endurance limit studied by
researchers, Garg et al. (2002) studied the relationship between endurance limits and
43
different MVC% from elbow flexion angles for 12 females. Results were as follows, as
shown in Figure 2-3 (Garg et al., 2002):
1. In general, inversely proportional continuous non-linear relationship between (a
decrease endurance limits during increase MVC %).
2. Up to (30%) of MVC%, High rate or decline in endurance limit with even increase in
MVC%.
3. From (30% to 90%) of MVC%, slower rate decline in endurance limit with even
increase in MVC%.
4. At (5%) of MVC, “curve does not become asymptotic” even at 5% of MVC and
different MVC percentages.
Figure 2-3 Endurance Limit Vs MVC% (Different Shoulder Posture)
44
Soylu and Arpinar-Avsar (2010) searched biologically the fatigue and MVC relationships
on 12 subjects
biceps brachii muscles (BBM), by using the surface electromyography
(SEMG) signals. He found that minor increase in the force with biceps brachii muscles
(BBM) can reach the maximum force level (MVC) within two seconds only. Rohmert
(1960) searched and proposed an endurance limit model for partial MVC (15% of MVC)
on the following assumptions where he assumed that subject can sustain 10-15 minutes
static contraction sustained without tiring. The relation between MVC% and endurance
limit where higher of MVC% which lead to decreased endurance limit as shown in Figure
2-4.
Figure 2-4 Endurance Limit for Different % of MVC, Rohmert (1960)
Nag (1991), Rose et al. (1992), Sjogaard et al. (2000), and Garg et al. (2002) reviewed
Rohmert’s curve and stated that Rohmert’s curve was misjudging (i.e., overestimating)
45
the endurance limit duration at MVC low percentages. Bjorksten and Jonsson (1977)
researchers got similar results to Rohmert curve above between MVC holding force and
endurance time. Chatterjee and Chowdhuri (1991) searched the MVC force, (40%) of
MVC, in all age groups, and right hand (dominancy) relationship, he found that no
relationship between endurance values limit and MVC. Rohmert (1968) searched the
endurance limit and gender relationship, he stated that: 1) No important difference found
between females and males isometric muscle strength endurance time and 2) Endurance
limit is independent of the subject sex with requirement that they work at same MVC.
Eksioglu (2011) researched the static force endurance limits including more variables
(anthropometric variables, BMI, grip span) in a trial to get a comprehensive model and
compared it with the other research models. He got the following results at 30% MVC.
Other research models got altered values (lower or higher) and found similar results for
other
percentages
as
shown
in
Figure
2-5
by
Eksioglu
(20
Figure 2-5 Endurance Limit Of 40% of MVC of Left Hand and Right Hands of 93
Men (Chatterjee & Chowdhuri, 1991)
46
Garg et al. (2002) searched the fraction of MVC and endurance level for different (elbow
flexion angles) and revealed the following:
A. Nonlinear relationship between MVC force and time according to Minnal,
2014 (The curve is never asymptotic).
B. Inversely proportional between MVC and Endurance limit for certain elbow
flexion angle (increase the Fraction MVC resulted in Reduce Endurance Limit).
C. From 15% to 30% MVC, the endurance limit decreasing in high rate.
D. 30% -90% MVC the endurance limit decreasing in slower rate increased
rapidly.
Chatterjee and Chowdhuri (1991) searched the fraction MVC at 40% level where they
found: 1) 40% MVC level independent of gender and age and 2) Dominant hand
sustained extra endurance limit average of 16 seconds more than the non-dominant hand.
Mogk and Keir (2003) measured the forearm fatigue response for forearm posture and
wrist combinations for different fractions of MVC (5%, 50%, 70%, 80%, and 100%)
with three forearm postures (neutral, pronated, and supinated) and different wrist postures
(extended, flexed, and neutral) and, results were as follows: 1) Wrist postures flexed
affected grip force with different forearm posture, 2) Other wrist postures got altered
muscle contributions and 3) Wrist postures flexion got the highest muscle activation. Al
Meanazel (2013) used the psychophysical approach on 120 subjects (males and females)
to get a model for endurance limit (number of cycles to fatigue). Considering the
following independent factors: BMI, hand grip circumference gender, hand dominancy
and mode of contraction, he found out that the maximum endurance limit results for
nonsmoker male that have both, higher BMI and higher grip circumference, when using
the dominant hand.
Endurance fatigue and MVC effect on isometric endurance limit studied by several
47
researchers (Rohmert, 1960a, 1960b; Merton, 1654; Funderburk et al., 1974) stated that
when muscle contraction tension go beyond the (10-15% envelope) maximum voluntary
strength of the human muscle’s, the muscle fatigue rate increases rapidly. Chatterjee and
Chowdhuri (1991) researched the MVC force. 40% of MVC in all age groups, and right
hand (dominancy) relationship, he found no relationship between endurance values limit
and MVC. Al Meanazel (2013) found out that the maximum endurance limit results for
nonsmoker male that have both (higher BMI and higher grip circumference) when using
the dominant hand. Rohmert (1960a; 1960b), Merton (1654), and Funderburk et al. (1974)
stated that in case of that isometric exercise, the muscle fatigue rate increased rapidly
specially when muscle contraction tension go beyond the (10%-15% envelope) of the
maximum voluntary strength of the human’s muscles. Rohmert (1968) researched the
endurance limit and gender relationship. He stated that no important difference was found
between females and males isometric muscle strength. Endurance time and endurance
limit is independent of the subject’s sex, with requirement that they work at same MVC.
Kumar et al. (1991), Mital and Genaidy (1989), Mital et al. (1986) stated that isometric
muscle strength cannot be considered as a good predictor or indicator of general body
health or strength. Mital and Faard (1990) also have similar points of view as Kumar et al.
(1991) and Mital and Genaidy (1989). Mital et al. (1986) that isometric muscle strength
cannot be considered as a good predictor because of the absence of body movement and
segment throughout the maximal MVC. Funderburk et al. (1974) mentioned that for
isometric strength at 15% of MVC, holding time did not vary between high and low BMI
persons.
Fatigues differences between individuals (stronger and weaker) effect on isometric
48
endurance limit studied by researchers, Caldwell (1964) and Start and Graham (1964)
researched the effect of fatigue on both stronger and weaker individuals. He found that
the different levels of grip strength holding time don’t vary between weaker and stronger
individuals when subjected to the same load and this might happen because loads used
were small loads, for Kroll (1968) Mundale (1970) the story was different when both
weaker and stronger persons subjected to (medium to high) MVC levels. The weaker
person maintained better endurance in activities especially at high MVC%. Miller et al.
(1993) researched the relationship between muscle characteristics and the strength, he
found that: 1) Males are stronger than females, females got 52-66 % of male strength and
2) Males are stronger because of muscle fibers size and distribution where females have
less lean tissue in the upper body. Fatigue between left and right (dominancy) effect on
isometric endurance limit studied by researchers, Chatterjee and Chowdhuri (1991)
searched the MVC force, 40% of MVC, all age groups, and right hand (dominancy)
relationship. The research resulted in the following: 1) The right hand contraction
endurance limit found meaningfully greater than left hand contraction endurance limit,
and 2) Right and left hand contraction endurance limit different by 16 seconds. Chatterjee
and Chowdhuri (1991) researched the fraction MVC at 40% level where they found that
the dominant hand sustained extra endurance limit an average of 16 seconds more than
the non-dominant hand. Al Meanazel (2013) found out that the maximum endurance limit
results when using the dominant hand.
Endurance and (height, weight, BMI and body surface area) effect on isometric
endurance limit studied by researchers, Chatterjee and Chowdhuri (1991) searched the
MVC force, 40% of MVC, of all age groups, and right hand (dominancy) relationship. He
49
found that no relationship between endurance values were limited by height, weight, and
body surface area, as per the results shown in Figure 2-6. Chatterjee and Chowdhuri
(1991) and Funderburk et al. (1974) mentioned that for isometric strength at 15% of
MVC, holding time did not very between high and low BMI persons. Dennerlein et al.
(2002) searched the forearm fatigue and repetitive task relationship using the
psychophysical approach. Al Meanazel (2013) found out that the maximum endurance
limit results for nonsmoker male that have both higher BMI and higher grip
circumference when using the dominant hand. Sherif et al. (2012) performed a study in
Indian males and found a positive correlation between higher body physical factors
(forearm anthropometric BMI, and hand muscle) with hand grip strength in Indian males.
Endurance limit and gender effect on isometric endurance limit studied by researchers,
Rohmert (1968) searched the endurance limit and gender relationship, he stated that: 1)
No important difference found between
females and males isometric muscle strength
endurance time and 2) Endurance limit is independent of the subject sex with requirement
that they work at same MVC
Carlson (1969) and Miller et al. (1993) found that males withstand greater absolute forces
than females at target force. Hunter and Enoka (2001) researched the relative reductions
in MVC force at exhaustion and found no difference between males and females where
they had parallel reductions in Maximum voluntary contraction at MVC force at
exhausting state. Chatterjee and Chowdhuri (1991) researched the fraction MVC at 40%
level where they found 40% MVC level independent of gender and age. Al Meanazel
(2013) found out that the maximum endurance limit results for nonsmoker males that
have both (higher BMI and higher grip circumference) when using the dominant hand.
50
Miller et al. (1993) researched the relationship between muscle characteristics and the
strength. He found that males are stronger than females. Females got 52-66% of male
strength and males are stronger because of muscle fibers size and distribution where
females have less lean tissue in the upper body. Static force endurance limit effect on
isometric endurance limit studied by researchers, Eksioglu (2011) researched the static
force endurance limits including more variables (anthropometric variables, BMI, grip
span) in atrial to get a comprehensive model and compared it with the other researcher’s
models. He got the following results: at 30% MVC other researcher’s models get altered
values (lower or higher) and similar for all other percentages. Chatterjee and Chowdhuri
(1991) researched the fraction MVC at 40% level where they found 40% MVC level both
independent of the age and or gender.
Figure 2-6 Endurance Limits Of 40% of MVC of Left Hand and Right Hands
51
Endurance and gender influence on isometric endurance limit studied by researchers
Chatterjee and Chowdhuri (1991) researched the fraction MVC at 40% level where they
found 40% MVC level independent of gender and age. And finally endurance approaches
and different devises used in researches, Dennerlein et al. (2002) searched the forearm
fatigue and repetitive task relationship using the psychophysical approach. Mogk and
Keir (2003) measured the forearm fatigue response for forearm posture and wrist
combinations using the EMG (Electromyography). Al Meanazel (2013) used the
psychophysical approach on 120 subjects (males and females) to get a model for
endurance limit (number of cycles to fatigue). Dennerlein et al. (2002) found that
repetitive tasks have a great effect on the forearm fatigue.
2.3 ISOTONIC MUSCLE FATIGUE
Isotonic muscle fatigue deals with the exerted amount of force and speed limits. The IMF
researches were limited and inadequate in the biomechanical and physiological
approaches mostly used to build the isotonic muscle fatigue. The first IMF researches
conducted in early 19th century were Fenn and Marsh (1935) on cat and frog subjects.
They researched the force application and velocity relationship and got an exponential
curve relationship. Garg and Beller (1994) found that speed is a very important factor for
isotonic muscle strength. Al Meanazel (2013) used the psychophysical approach on 120
subjects (males and females) to get a model for endurance limit (number of cycles to
fatigue). Considering the following independent factors (BMI, hand grip circumference
gender, hand dominancy and mode of contraction), he found out that the maximum
endurance limit results for nonsmoker male that have both (higher BMI and higher grip
circumference) when using the dominant hand.
52
53
2.4 ISOKINETIC MUSCLE FATIGUE
Garg and Beller (1994) were among first researchers for isokinetic and isotonic muscle
strength, they got the following results: 1) In isotonic muscle strength, the speed has a
major rule, 2) Conflict between isokinetic lifting capability and individual subjective
judgment of physical stresses where appeared in determining the speed of lifting and 3)
The high speed lifting more comfortable than slow speed lifting which is contrary to
ergonomics principles
Yessierli et al. (2009) researched the isokinetic muscle strength for 24 subjects that have
two major groups: young (18-25 years) and old (55-65 years). They wanted to simulate
common material handling tasks and they looked for a model that included the following
parameters: gender, age, and task data, for extended isokinetic repetitive intermittent
torso exercises until exhaustion. Yessierli et al. (2009) used the electromyographic
(EMG) signals, MVC readings, and subject perceived discomfort to evaluate fatigue
progression. He used fraction MVC (30 to 40% of MVC) and (30-60) seconds duty cycle.
They got the following results
A. Younger group have 23% more MVC than older group.
B. Marginal fatigue effect on gender and age.
C. Significant interactive fatigue effects of both gender and age on effort level.
Where older ages group should have tasks with lower effort levels.
D. Endurance time will be reduced by 30% for loads increased by 10%.
Garg et al. (1994) used the psychophysical approach by using the bio kinetic ergometer
with load cell and low back subjective perception. They used 9 male subjects in their
54
experiment to find out the relationship between isokinetic lifting strengths and the speed
of lifting. They found the following results:
A. Inversely proportional between the isokinetic lifting strengths (Peak and mean)
with lifting speed.
B. Inversely proportional between the isokinetic lifting strengths (Peak and mean)
with box width.
C. Box width has less effect than Lifting speed.
D. High speed lifting more comfortable than low speed lifting (subjects
judgment).
E. Maximum allowable load is equal to high speed lifting and according to above
results, they recommended that job task designed for best lifting speed and box
widths combination to get the optimal isokinetic lifting strength and least workers
complaints.
F. The larger hand grip circumference can be contributed to a mechanical
advantage.
55
2.5 GRIP STRENGTH NEW RESEARCH AREAS
According to Kilgour et al., (2010), a new study performed by Concordia University at
the McGill Nutrition and Performance Laboratory on 203 patients with advanced-stage
cancers, finds an important relationship between individuals
handgrip strength and
cancer rate survival. The researchers find that simple person handshake (simple squeeze)
can tell and reveal a lot of information about an individual's attitude and character and
stated that beside using it as a “diagnostic tool to gauge strength and quality of life
among critical patients”, it can measure the individuals capability and ability to battle the
deadly disease. A study was performed by letting the advanced-stage cancer patients use
their dominant hand to squeeze a dynamometer and measure the maximum exerted MVC
(the patients peak grip strength). According to researcher, Kilgour et al., (2010), "this
measure is one of several to categorize patients according to the severity of their disease.
It can help determine interventions they may need, whether clinical, nutritional or
functional". Kilgour et al. (2010) also stated that the study may result in that the
handgrip test could be used as a “better alternative” to measure and find out the
participants body strength and their decline rate than other traditional used ways like
decreasing body weight. The Handgrip strength MVC test will predict the patients
survival rates “associated with changes in body composition, nutritional status,
inflammation, and functional ability in several chronic disease conditions". This test will
guide cancer patients to enhance “their physical and mental health by engaging in
physical activity and eating healthier”. According to a study done by Sirajudeen et al.
(2012) they found positive correlation between the males physical factors like body mass
index, weight, height, beside hand anthropometric measurements, and grip strength. By
using this, they stated that the grip strength assessment results were considered and
56
accepted as good indicators of “nutritional status, bone mineral content, muscular
strength and functional integrity of upper extremity”. They also have a strong role to
measure treatment strategy results of hand.
According to a study performed by Sanderson WC, Scherbov (2014), they used data from
the Health and Retirement Survey (HRS) from two groups, less and higher than high
school diploma level, and “studied regressions on hand-grip strength that were run for
each sex and race using age and education. Their interactions and other covariates, as
independent variables”, they found that
“hand-grip strength produces an easily
interpreTable, physical-based measure that allows us to compare characteristic-based
ages across educational subgroups in the United States”. They also found “a strong
handshake can indicate power, confidence, health, or aggression”. “The strength of a
person’s grip may also be a useful way to measure true age”. They found that the handgrip strength testing results be used as a dependable predictor measurement of the human
population aging “future mortality, morbidity, cognitive decline
and the ability to
recover from hospital stays. Detailed findings as follows: “the hand-grip strength of 65
year old white males with less education was the equivalent to that of 69.6 (68.2, 70.9),
year old white men with more education, indicating that the more educated men had aged
more slowly”. According to Garg (1994), new research studies about physical activity
effect on middle-age in Boston Medical Center, discussed through American Academy
of Neurology's" annual meeting (2015) find relation between hand grip strength and
walking speed for 2,400 people during 11 years. Results found that “a slower walking
speed in middle age people were one-and-a-half times more likely to develop dementia
compared to people with faster walking speed and people with a stronger hand grip was
57
associated with a 42 percent lower risk of stroke in people over age 65. “ This may assist
the physicians to determine risk of developing dementia or stroke for middle-aged
peoples.”
According to Swift et al. (2012) in this researchers objective was to “to assess how agerelated social comparisons, which are likely to arise inadvertently or deliberately during
assessments, may affect older people's performance on tests that are used to assess their
needs and capability”. They used participants from UK centres and senior's lunches in the
south of England. They established the normal hand grip strength values data and
checked relations with the anthropometric factors by testing 132 participants using the
jamar dynamometer. They found the following: “ right hand and dominant hand gs were
found to be higher and statistically significant compared to left hand and nondominant
hand gs. Respectively, men had higher values of gs compared to women. A negative
association was observed between age and dominant hand gs, and a positive association
was documented between height and dominant hand gs; while the respective comparison
for weight and dominant hand gs documented a statistically significant positive
association only in the male group. A positive association between bmi and dominant
hand gs was seen in female individuals. “Additional factors associated with gs should be
the goal of future investigations.” As a conclusion, they found that “due to the potential
for age comparisons and negative stereotype activation during assessment of older people,
such assessments may underestimate physical capability by up to (50%), because age
comparisons are endemic. This means that assessment tests may sometimes seriously
underestimate older people's capacity and prognosis, which has implications for the way
58
healthcare professionals treat them in terms of autonomy and dependency”. According to
Swift
et al., (2012), the key messages of the study are as follows: “
1. Psychosocial factors may influence how strongly physical effects of ageing
manifest themselves.
2. Age comparison creates a stereotype threat, which can reduce older people's
hand grip strength by up to (50%), as large as the normal range from middle to
old age.
3. Healthcare professionals should be aware of the potential for age comparison
and stereotypes to affect outcomes of assessments of older people.
4.
Hand grip is an ‘objective measure’ of physical capability among older
people. It is predictive of frailty, morbidity, disability and mortality.
5. This first experimental test of the impact of age comparison on older people's
hand grip strength demonstrates that it is impaired by comparison with younger
people.
6. This research was conducted in a non-medical setting and involved
participants in good health with a small convenience sample. However the effects
remain significant even when age, gender, education, degree of arthritis in the
hands, type of residence and location of testing are accounted for.
7. Further research is needed to evaluate the prevalence of age comparisons in
clinical testing settings, and effects on people of different ages.
59
8. Other studies about Assessment of Muscle Status In Chronic Kidney Disease
Patients Using Hand Grip Strength (HGS) Tool And Body Composition Monitor
( BCM) in Cario University” .
60
CHAPTER THREE RESEARCH METHODOLOGY
3.1 INTRODUCTION
This chapter explains the methodology and procedures used to measure and evaluate the
maximum voluntary contraction, and the fatigue and endurance limits (number of cycles
and time until fatigue) for both isometric and isotonic strength of subjects from the
profession of aviation mechanics. Before the actual experiment officially started, a pilot
study was conducted to qualify the experimental independent and dependent variables,
and to evaluate the apparatus used and experimental procedure.
3.2 EXPERIMENTAL ELEMENTS
The subjects were 132 retired and active-duty mechanics from the Royal Jordanian Air
Force. All of them participated in each of the three tests (maximum voluntary contraction
(MVC), isometric muscle fatigue test, and isotonic muscle fatigue test). Subjects were
healthy males who did not have any physical injuries related to the hand. Anthropometric
measurements have been collected. Descriptive statistics are shown in Table 3-1 for
subjects in the three experiments.
Table 3-1 Descriptive Statistics of Aviation Male Subjects
Variable
Mean
Standard Dev
Minimum
Age (Y)
41.712
7.833
25.000
Maximum
65.000
Weight (Kg)
Height (M)
82.600
1.7581
12.850
0.0705
55.000
1.5500
114.00
1.9300
BMI
HGC (CM)
26.679
22.523
3.600
1.338
18.711
19.500
37.422
25.500
FAC (CM)
29.341
2.441
23.000
35.000
61
The dependent and independent variables are shown in Table 3-2 with their levels. They
included most of researched variables during the last sixty years and included many new
variables as well.
Table 3-2 Dependent, Independent Variables and Treatment Levels
Independent
Dependent Variable
Treatment Level
Variable
Age (years)
1)
2)
3)
4)
5)
6)
A0: (25 – 30)
A1: (30 – 35)
A2: (35 – 40)
A3: (40 – 45)
A4: (45 – 50)
A5: (> 50)
Trade
1)
2)
3)
4)
5)
APG: (Airplane General)
E&I: (Electrical and Instrument)
COMNAV: (Command & Navigation)
Eng: (Engine)
GSE: (Ground Support Equipment)
1- MVC
2- Isometric Endurance
Limit (20%, 40%, 60%
Smoking
and 80%)
3- Isotonic Endurance Limit
Body Mass
(20-60%)
Index
Fixed Factors
(BMI)
1- Jordanian Subjects
2- Digital Dynamometer
1) Smokers
2) Nonsmokers
1) Small: S (19 – 25)
2) Medium: M (25 – 30)
3) Large: L (> 30)
Hand Grip
Circumferen
ce (CM)
1) Small: S (<= 21.5)
2) Medium :M (21.5 – 23.5)
3) Large: (> 23.5)
Hand
Dominancy
1) D: Dominant
2) ND: Non Dominant
Forearm
Circumferen
ce (CM)
1) Small: S (<= 27.5)
2) Medium: M (27.5 – 31)
3) Large: (> 31)
Posture
1) Sitting : SIT (Sitting)
2) Standing: STD (Standing)
Height (M)
1) Short: S (<= 1.70)
2) Medium: M (1.70 – 1.81)
3) Tall: T (> 1.81)
62
The apparatus used in this experiment was digital Camry Hand grip Dynamometer
(Figure 3-1) to measure both the Maximum Voluntary Contraction (MVC in Kgs) and
endurance limit (test time to fatigue, Seconds). This apparatus has an adjustable grip to
suit subjects hand circumferences. Gollehon extendable Goniometer (Figure 3-1) was
used to assess and set the subjects’ joint angles at 90 degrees for elbows, knees, and hip
in the sitting position. A measuring tape was also used to measure the height and the hand
GC. A digital stop watch was used to record the endurance limit (the time was recorded
to the nearest second). Finally, a digital scale was used to measure weights (rounded to
the nearest 0.1 Kgs).
Figure 3-1 Experimental Instruments
The overall research methodology is shown in Table 3-3, which describes the procedures
to conduct the maximum voluntary contraction test (MVC) (Kgs), and isometric muscle
fatigue limit test (time to fatigue; Seconds), and isotonic muscle fatigue test (time to
fatigue; Seconds).
63
Table 3-3 Overall Research Methodology for Aviation Subjects
Start
Participants
132 Air force technicians (Retired/Active, Male, Jordanian)
Gather Anthropometric Data (Independent Variables)
1. Age
2. Weight
3. Hand grip circumference (GC)
4. Smoking conditions
5. Dominant hand
6. Height
7. Trade
8. Forearm Circumference
9. Calculated BMI
10. Dominancy
11. Forearm circumference
Maximum Grip Strength Test
1.
2.
3.
4.
Seated with 90 joint angles (hip, knees, and elbows)
Hand grip Dynamometer adjusted to fit the GC
Each subject to exert maximum force on the Dynamometer
Do three Maximum Grip strength tests (for MVC) for 5 seconds each with
dominant hand, and 5-minute rest.
5. Repeat Steps 1-5 for the non-dominant hand
Isometric Endurance Limit Test
1.
2.
3.
4.
5.
6.
Seated with 90 joint angles (hip, knees, and elbows)
Hand grip Dynamometer adjusted to fit the GC
Hold at 20%, 40%, 60% and 80% of MVC until fatigue (e.g., 25%, 50%, 75%)
Rest for 5 minutes
Have heart rate recorded
Repeat Steps 1-5 for the non-dominant hand
Isotonic Endurance Limit Test
1. Seated with 90 joint angles (hip, knee, and elbow)
2. Hand grip Dynamometer adjusted to fit the GC
3. Move pointer Hand grip Dynamometer between 20% and 60% as fast as
possible until fatigue.
4. Rest for 5 minutes
5. Repeat Steps 1-4 for the non-dominant hand
Repeat The Above Three Tests for Standing Posture
64
3.3 EXPERIMENTAL PROCEDURE
The objective of the research is to find and verify the major factors that affect static and
dynamic grip forces in exertion and obtain the measurements for (1) MVC; (2) isometric
muscle fatigue limit test (20%, 40%, 60% and 80%) for the time to fatigue (Seconds);
and (3) isotonic muscle fatigue test (20-60%) for the time to fatigue (Seconds). Subjects
participate in the experiment at the same time and under the same conditions. The
following paragraph explains the detailed steps of the experiment. Figure 3-2 shows the
subject’s posture during the experiment.
Figure 3-2 Subject Posture during the Tests
The MVC experiment was performed on all the 132 participants as follows:
1. Measure height and weight by using a digital scale and a measuring tape. Each
aviation subject was asked to sit on an adjustable chair and several volunteers were asked
to make sure that their joints (hip, knees, and elbows) maintain 90˚ angel by using the
Gollehon extendable goniometer. Also, they were asked to keep their feet on the floor.
Then, the subject’s hand HGC and FAC radius were measured using the dynamometer
fixed scale where subjects should match grip size and then do the following:
65
A. Determine the initial maximum voluntary contraction (MVC) force when the
subject is at rest and in a neutral posture.
B. MVC was measured by telling the subject to hold the hand grip dynamometer
using one hand with the dynamometer scale hidden from the subject (to measure
the voluntary contraction without over exertion).
C. Each subject performed three MVCs for 5 seconds for each trial and with 5minute resting period.
D. Above procedure repeated for each hand to check dominancy effect.
E. Record the maximum peak MVC for each subject.
F. Repeat above procedure for standing position.
In the isometric muscle fatigue test, the aim was to measure the endurance limit for
isometric muscle strength at the following percentages of MVC: 20%, 40%, 60%, and
80%. The experiment was similar to the MVC test as follows:
A. Record subject’s height and weight by using a digital scale and a measuring tape.
B. Each subject was asked to sit on an adjustable chair, and volunteers were asked to
make sure that their joints (hip, knees, and elbows) are at 90˚ by using the Gollehon
extendable goniometer. Subjects were asked to keep their feet on the floor.
C. Measure the subject’s hand GC and FAC radius with dynamometer fixed scale
where each subject should match grip size.
D. Subject was asked to hold the hand grip dynamometer with one hand at a time at
each designed percentage (20%, 40%, 60% and 80%), and was asked keep holding
each partial MVC until pain and feeling of fatigue starts in their arm.
E. Record the time in seconds to fatigue for each partial MVC.
F. Give subject a 5-minute break.
G. Repeat above procedure for standing position.
In the isotonic muscle fatigue test, the aim was to measure the endurance limit for
isotonic muscle strength at the following percentages of the MVC: 20-60%. The
experiment was similar to the MVC test as follows:
66
A. Record subject’s height and weight by using a digital scale and a measuring tape.
B. Each subject was asked to sit on an adjustable chair, and to make sure that their
joints (hip, knees, and elbows) are at 90˚ by using the Gollehon extendable
goniometer. Subjects were asked to keep their feet on the floor.
C. Measure the subject’s hand GC and FAC radius on dynamometer fixed scale
where each subject should match grip size.
D. Subjects were asked to move the moving scale on the hand grip dynamometer
continuously without stopping at any force between 20% and 60% of MVC until
they start feeling pain and fatigue in their arm in two phases.
E. Fast mode (as fast as possible between 20% and 60%).
F. Slow mode (at normal speed between 20% and 60%).
G. Record the number of cycles to fatigue for each partial MVC which are used as an
indication of isotonic muscle fatigue limit.
H. Give subject a 5-minute break.
I. Repeat for hand dominancy
J. Repeat above procedure for standing position
67
3.4 DATA ANALYSIS AND MODELING
The analyses conducted in this research are listed in Table 3-4. The analysis included an
analysis of variance (ANOVA) (using Minitab 17) followed by the use of different
modeling techniques to build models (to predict MVC, isometric endurance limit, and
isotonic fatigue limit) using Minitab 17 and Matlab 15.
Table 3-4 Data Analysis and Modeling Methodology
MVC data
Isometric Muscle Fatigue
Isotonic Muscle Fatigue
(Endurance Limit Data)
(Endurance Limit Data)
Descriptive statistics (Model Adequacy Checks)
Perform MANOVA
for dependent variables (MVC, Endurance limit and no. of cycles to fatigue) and for
independent variables
Perform ANOVA
for dependent variables (MVC, Endurance limit and no. of cycles to fatigue) and for
independent variables
Develop Linear Regression (LR) Models
Develop Non-Linear Regression (NLR) Models
Develop Neural Network Model
Develop Adaptive Neuro Sugeno Fuzzy Inference System (ANFIS) Model
68
CHAPTER FOUR RESULTS AND DISCUSSION
4.1 INTRODUCTION
As previously mentioned, the experiments were performed on 132 male subjects (20 to
60 years old) that represent retired and active-duty engineers and technicians from the
Royal Jordanian Air Force. The data was presented, analyzed and discussed in this
chapter. As previously mentioned, the dependent variables are as follows:
1. Maximum voluntary contraction (MVC) test: MVC (in Kgs).
2. Isometric muscle fatigue limits test: endurance limits (in Seconds) for different
MVC ratios (20%, 40%, 60% and 80%).
3. Isotonic muscle fatigue test: endurance limits (in Seconds) at both high and low
speeds.
The dependent variables and independent factors are shown in Table 4-1.
Table 4-1 Dependent and Independent Variables with their Levels
Dependent Variables
Treatment Levels
Independent
Variables
4- MVC
5- Isometric Endurance
Limit (20%, 40%, 60%,
80%)
6- Isotonic Endurance
limit (20-60%)
Fixed Factors
Age (years)
1.
2.
3.
4.
5.
6.
Trade
1. APG: Airplane General
2. E&I: Electrical and
Instrument
3. COMNAV: Communication
& Navigation
4. Eng: Engine
5. GSE: Ground Support
Equipment
3- Jordanian Subjects
4- Digital Dynamometer
69
A0: (25-<30)
A1: (30-<35)
A2: (35-<40)
A3: (40-<45)
A4: (45-<50)
A5: Above 50
Smoking
1. Smokers
2. Non-smokers
Body Mass Index
(BMI)
1. Small: S (19-<25)
2. Medium: M (25-<30)
3. Large: L above =>30
Hand Grip
Circumference (CM)
1. Small: S (=< 21.5)
2. Medium: M (>21.5 -23.5)
3. Large: above 23.5
Hand Dominancy
1. D: Dominant
2. ND: Non-Dominant
Forearm
Circumference (CM)
1. Small: S (<= 27.5)
2. Medium: M (>27.5-31)
3. Large: L (above 31)
Posture
1. Sitting: SIT
2. Standing: STD
Height (M)
1. Short: S (<= 1.70)
2. Medium: M (>1.70-1.81)
3. Tall: T (above 1.81)
For each section, experiment results were analyzed in the following manner. First,
descriptive statistics were provided. Then, correlation analysis, normality test, and outlier
analysis were conducted. Since several dependent variables were considered in this study,
multivariate analysis of variance (MANOVA), using Minitab 17, was conducted in this
study. The MANOVA table provides the following results: Wilks' test, Lawley- Hoteling,
Pillai’s and Roy's test. Analysis of variance (ANOVA) was also conducted. In addition,
linear and non-linear regression models were developed and compared using stepwise
procedures with both forward and backward selections. Forward selection starts with the
assumption of no predictors in the model. It is important to note that because of nature of
the experiment and expected multicollinearity issues, a general linear model were
70
developed using MATLAB 15.Finally, an artificial neural network (ANN) model was
developed using neural network toolbox in MATLAB 15. In addition, an Adaptive
Neural Fuzzy Inference System (ANFIS) using a Sugeno FIS was also developed.
4.2 GENERAL DESCRIPTIVE STATISTICS
Correlations between independent and dependent factors were computed with Minitab
17, by using Pearson product moment since the independent factors were continuous
variables. Then, interval plots were used for better understanding of those relationships.
As expected, correlations existed between physical factors of the human body (e.g., FAC
with HGC, BMI, and Weight). Negative correlations between some independent factors
were noticed such as age and height. Another negative correlation is observed between
MVC and Isotonic endurance limit. A normality test showed that all independent factors
were normally distributed as shown in the graphical plot of normal probabilities. This
indicates no need for any transformation, except in isometric endurance limit cases.
Therefore, a transformation function box (i.e., cox with max Lambda) was applied since
the data are in subgroups. The descriptive statistics were provided in Tables 4-2 and 4-3.
Table 4-2 Overall Summary Data
Variable
Mean
Age (Y)
41.712
Weight (Kg)
82.60
Height (M)
1.7581
BMI
26.679
HGC (CM)
22.523
FAC (CM)
29.341
Standard Dev
7.833
12.85
0.0705
3.600
1.338
2.441
71
Minimum
25.000
55.00
1.5500
18.711
19.500
23.000
Maximum
65.000
114.00
1.9300
37.422
25.500
35.00
Table 4-3 Descriptive Statistics (Dependent Factors)
Variable
Mean StDev
MVC(Kg)
46.718 8.456
Isometric End Limit (20%; Sec)
167.45 61.76
Isometric End Limit (40%; Sec)
73.12 35.51
Isometric End Limit (60%; Sec)
38.371 21.838
Isometric End Limit (80%; Sec)
21.748 13.623
Isotonic End Limit (20%-60%; Sec)
36.291 17.648
Minimum
17.100
60.00
21.00
9.000
5.000
Maximum
81.600
343.00
203.00
116.000
93.000
6.000
110.000
4.3 MULTIVARIATE ANALYSIS OF VARIANCE (MANOVA)
Multivariate analysis of variance (MANOVA) with Minitab 17 was conducted with 0.05
significance level, since there are two or more dependent variables. In this experiment,
there are three main tests (MVC, isometric and isotonic muscle fatigues) with nine
detailed independent factors. MANOVA detects and tests the effect of the independent
factor combinations on all dependent factors (responses). The hypothesis is that none of
the nine independent variables has any effect on the four dependent factors (responses).
The MANOVA results are shown in Table 4-4 with significant level of 0.05, indicating
that further analysis by MANOVA is necessary on these significant factors. However, the
research study considers all factors. It shows the significant factors (MVC, Isometric and
Isotonic End, Limit) for each of the analysis for each of the MANOVA tests (i.e., Wilk’s,
Lawley-Hotelling and Pillai’s). Table 6 also demonstrates that further analysis is needed
by ANOVA on the significant factors for better evaluation of effect of each factor on the
responses.
72
Table 4-4 MANOVA for Experiment Terms
Test
Factor
Wilk’s
LawleyHotelling
Pillai’s
Age
0.000
0.000
0.000
Height
0.000
0.000
0.000
Trade
0.036
0.036
0.036
MVC
Isometric End, Limit (20%)
(40%) (60%) (80%)
Isotonic End, Limit (20%60%)
HGC
0.000
0.000
0.000
BMI
0.001
0.001
0.001
FAC
0.00
0.00
0.00
Trade
0.000
0.000
0.000
HGC
0.000
0.000
0.000
FAC
0.000
0.000
0.000
Age
0.002
0.002
0.002
FAC
0.027
0.027
0.027
Trade
.000
.000
.000
HGC
0.019
0.019
0.019
Height
0.036
0.036
0.036
The MANOVA was repeated using all dependent factors as responses and the same
significance factors were obtained as shown in Table 4- 5.
73
Table 4-5 MANOVA for All Dependent Factors
Analysis
Factor
Wilk’s
MVC, Isometric and
Isotonic End, Limit
Lawley-Hotelling Pillai’s
Age
0.000
0.000
0.000
FAC
0.000
0.000
0.000
Trade
0.000
0.000
0.000
HGC
0.000
0.000
0.000
Smoking
0.003
0.003
0.003
Height
0.000
0.000
0.000
BMI
0.000
0.000
0.000
ANOVA has been conducted for each section to identify and confirm the significant
factors. It is found that general linear model (GLM) could not take all categorical
independent variables. Because of the multicollinearity issue, the stepwise GLM
ANOVA is considered as an extra procedure. Significant factors were chosen with
additional prior knowledge about them from the literature taking in consideration that this
data is subjective judgment of participants (human social experiment). Linear regression
models were developed with both general and stepwise methods (to avoid possible
stepwise pitfalls) using MINTAB 17 for each case of MVC. The general linear and
nonlinear regression models were built using the MATLAB 15, considering all factors as
significant.
74
4.4 BASIC ANALYSIS
In the following sections, analysis and discussion of the experiment will be introduced for
all experimental terms, starting by reminding of most related important literature
followed by the descriptive statistics. It also includes general and specific factors, interval
plots, ANOVA with full factorial design of experiment (DOE), and regression analysis.
In the linear regression prediction model, residual analysis plots were generated with
MINTAB 17. MATLAB 15 will be used to find the general linear and nonlinear
regression equations since after initial and thorough review of ANOVA, the literature and
descriptive statists, we decided to include all six independent factors in the predicted
general linear and nonlinear regression equations, specifying equations for each case. In
the section of general descriptive statistics, all experimental terms are included to
describe and summarize data before drawing main conclusions. Interval plots were used
to compare variability intervals for the experimental subjects and summary of central
tendency using a 95% confidence level in different groups of MVC in different
experimental terms. ANOVA has been performed to identify and confirm the significant
factors. Since the general GLM could not take all categorical independent variables, this
dissertation also considered stepwise GLM ANOVA. Significant factors were chosen
with prior knowledge taking in considerations that they are subjective judgment of
participants (human social experiment). Linear regression models were developed for
general as well as stepwise methods to overcome stepwise pitfalls. Using MINTAB 17
for each case of MVC, the general linear and nonlinear regression models were
developed using te MATLAB 15 considering all factors as significant. The standard error
of the regression (S) as a measure of model fit in ANOVA shows lower standard error.
75
The coefficient of determination (R-squared) shows acceptable models that explain and
fit experiment data. Residual plots show that model fit in ANOVA and regression
analysis are satisfactory. The normal probability plot of residuals shows that the
independent variables follow normal distributions since the residues form a straight line.
The plot of residuals against fitted values tests the constant variance assumption and
shows the residuals are on both sides of the graph, with no data points deviating from the
majority of points. Histogram of the residuals shows the general characteristics of
experimental data and plots the residuals that include typical values, spread and shape.
Finally, the relationship between residuals and order of data shows the correlation
between collected data.
4.5 Maximum Voluntary Contraction Analysis and Discussion
The ANOVA results on the MVC experiment data are presented in this section, in
addition to the predicted general linear and nonlinear models for maximums voluntary
contraction (MVC) in different posture (sitting and standing) and both hands (dominant
and non-dominant). Note that different experimental conditions are symbolled as MVC
(Kg, Sit, D), MVC (Kg, Sit, ND), MVC (Kg, Stand, D), MVC (Kg, Stand, ND). ANOVA
with 95% confidence level was used to test the effects of the independent factors.
Hypothesis is presented as none of the experiment independent variables have any effect
on the output dependent variable. Model adequacy checks were tested for MVC data and
found that assumptions are met for constant variance normality and independency. Table
4- 1 shows the dependent factors, independent variables with their levels.
76
The ANOVA using design of experiment with full general factorial regression analysis
was performed using MINTAB 17. Table 4-6 shows ANOVA general factorial regression
ANOVA output.
Table 4- 6 Factor Information for ANOVA General Factorial Regression
Dependent Variables
Treatment Levels
Independent
Variables
1- MVC
2- Isometric Endurance
Limit (20%, 40%, 60%,
80%)
3- Isotonic Endurance
limit (20-60%)
Fixed Factors
Age (years)
Trade
1) A0: (25-<30)
2) A1: (30-<35)
3) A2: (35-<40)
4) A3: (40-<45)
5) A4: (45-<50)
6) A5: Above 50
1) APG: Airplane General
2) E&I: Electrical and
Instrument
3) COMNAV: Communication
& Navigation
4) Eng: Engine
5) GSE: Ground Support
Equipment
1- Jordanian Subjects
2- Digital Dynamometer
Smoking
1) Smokers
2) Non-smokers
Body Mass Index
(BMI)
1) Small: S (19-<25)
2) Medium: M (25-<30)
3) Large: L (above =>30)
Hand Grip
Circumference (CM)
1) Small: S (=< 21.5)
2) Medium: M (>21.5 -23.5)
3) Large: L (above 23.5)
Hand Dominancy
1) D: Dominant
2) ND: Non-Dominant
Forearm
Circumference (CM)
1) Small: S (<= 27.5)
2) Medium: M (>27.5-31)
3) Large: L (above 31)
Posture
1) Sitting: SIT
2) Standing: STD
77
Height (M)
1) Short: S (<= 1.70)
2) Medium: M (>1.70-1.81)
3) Tall: T (above 1.81)
78
Table 4-7 ANOVA General Factorial Regression
Source
Model
Linear
Posture
Age (Cat)
Hand Dominancy (HD)
Trade
Smoking
Height (CAT)
BMI (Cat)
HGC (Cat)
FAC(Cat)
2-Way Interactions
Posture*Age (Cat)
Posture*H D
Posture*Trade
Posture*Smoking
Posture*Height (Cat)
Posture*BMI (Cat)
Posture*HGC (Cat)
Posture*FAC (Cat)
Age (Cat)*Smoking
H D*Smoking
H D*BMI(Cat)
H D*HGC (Cat)
H D*FAC (Cat)
Trade*Smoking
Trade*Height (Cat)
Trade*BMI (Cat)
Smoking*Height (Cat)
Smoking*BMI (Cat)
Smoking*HGC (Cat)
Smoking*FAC (Cat)
Height (CAT)*BMI (Cat)
Height (CAT)*HGC (Cat)
Height (CAT)*FAC (Cat)
BMI (Cat)*HGC (Cat)
Error
Lack-of-Fit
Pure Error
Total
DF
95
20
1
5
1
4
1
2
2
2
2
75
5
1
4
1
2
2
2
2
5
1
1
2
2
4
8
8
2
2
2
2
4
4
4
4
432
150
282
527
Adj SS
23613.4
6465.2
31.9
2323.3
7.4
1575.4
318.5
193.7
352.7
10.4
1322.1
8435.2
24.7
78.4
76.0
6.3
24.1
4.0
11.0
14.3
542.2
233.0
143.2
17.9
123.8
1060.6
2027.7
1548.0
1101.5
23.8
1109.9
310.5
2109.6
861.9
704.2
442.1
14065.9
8350.9
5714.9
37679.3
79
Adj MS
248.562
323.260
31.855
464.653
7.364
393.842
318.499
96.859
176.330
5.198
661.028
112.470
4.941
78.375
19.008
6.319
12.055
2.002
5.494
7.131
108.444
233.000
71.610
8.926
61.906
265.159
253.465
193.502
550.756
11.912
554.953
155.245
527.407
215.466
176.050
110.523
32.560
55.673
20.266
F-Value
7.63
9.93
0.98
14.27
0.23
12.10
9.78
2.97
5.42
0.16
20.30
3.45
0.15
2.41
0.58
0.19
0.37
0.06
0.17
0.22
3.33
7.16
2.20
0.27
1.90
8.14
7.78
5.94
16.92
0.37
17.04
4.77
16.20
6.62
5.41
3.39
P-Value
0.000
0.000
0.323
0.000
0.635
0.000
0.002
0.052
0.005
0.852
0.000
0.000
0.979
0.122
0.675
0.660
0.691
0.940
0.845
0.803
0.006
0.008
0.112
0.760
0.151
0.000
0.000
0.000
0.000
0.694
0.000
0.009
0.000
0.000
0.000
0.009
2.75
0.000
ANOVA showed that the significant factors are age, trade, smoking, height, BMI, and
FAC. It also showed that posture, dominancy, and hand grip circumference are nonsignificant. Interaction effects are found for age*smoking, HD*smoking, trade*height,
trade*BMI, smoking*height, smoking*BM, smoking*HGC, smoking*FAC, height*BMI,
height*HGC, height*FAC, BMI*HGC. Root Mean Square Error (RMSE) equals 5.7 with
R-sq being 62.67%; however, by nature it is a human social experiment, and nonsignificant factors found in this dissertation were found significant in many other studies.
The multicollinearity issue might arise and thus all significant factors are considered.
Then, linear regression equations were calculated for all independent factors. In Figure 41, residual plots consist of normal probability plot, uniform distribution Vs fits, uniform
distribution Vs order, and normal histogram shape distribution.
Figure 4-1 Residual Plots for MVC
80
Regression equations were extracted for both general linear regression and general
nonlinear regression. Table 4-8 shows these equations. Tables 4-9 and 4-10 show general
linear models (MATLAB 15) and general nonlinear models (MATLAB 15), respectively.
Table 4-8 MVC General Linear and Nonlinear Models (MATLAB 15)
MVC= -21.594 - 0.43487 AGE(Y) + 22.073
RMSE: 6.31
Linear
HEIGHT (M) - 0.36207 + BMI 0.14221 HGC (CM) R-Sq: 0.448,
Models
+ 1.8439 FAC (CM)
R-Sq,(Adj) 0.443
Non
Linear
Models
MVC= 13.786 -0.0051191 * AGE(Y)^2 + 6.0779*
HEIGHT(M)^2 -0.006859 *BMI^2 + 0.0028544*
HGC(CM)^2 + 0.030977* FAC (CM)^2
RMSE: 6.3
R-Sq: 0.451,
R-Sq,(Adj) 0.445
Tables 4-10 and 4-11 show specific detailed grip strength models for each case to enable
comparisons with other researchers who used limited number of independent factors.
Table 4-9 MVC General Linear Models (Detailed) (MATLAB 15)
Terms
MVC(KG)
(SIT, D)
MVC(KG)
(SIT, ND)
MVC(KG)
(STAND,
D)
MVC(KG)
(STAND,
ND)
Linear Regression Model
MVC (KG, SIT, D) = 160.73 - 0.3503 AGE(Y)+ 1.2514
WEIGHT (M) - 77.472 HEIGHT (M) - 4.1551 BMI - 0.082654
HGC (CM) + 1.5912 FGC(CM)
MVC (KG, SIT, ND) = 46.365 - 0.48188 AGE(Y) + 0.37769
WEIGHT (M) - 12.922 HEIGHT (M) - 1.5137 BMI 0.030941HGC (CM) + 1.7817 FGC(CM)
MVC (KG, STAND, D) = 223.95 - 0.36711 AGE(Y) + 1.5356
WEIGHT (M) - 120.56 HEIGHT (M) - 5.2587 BMI + 0.087088
HGC (CM) + 2.1649 FGC(CM)
MVC (KG, STAND, ND) = 188.63 - 0.46084 AGE(Y) + 1.2396
WEIGHT (M) - 102.54 HEIGHT (M) - 3.9155 BMI + 0.45526
HGC (CM) + 1.6245 FGC(CM)
81
Errors
RMSE: 5.89
R-SQ: 0.508
RMSE: 6.13
R-SQ: 0.478
RMSE: 5.74
R-SQ: 0.546,
RMSE: 6.76
R-SQ: 0.415,
Table 4-10 MVC General Non Linear Models (detailed) (MATLAB 15)
Terms
MVC(KG)
(SIT, D)
MVC(KG)
(SIT, ND)
MVC(KG)
(STAND,
D)
MVC(KG)
(STAND,
ND)
Non- linear Regression Model
MVC (KG, SIT, D)= 51.669 - 0.0042189 X1^2 + 0.0035843
WEIGHT (M) ^2 - 4.8952 HEIGHT (M) ^2 - 0.039761 BMI^2 0.001795 HGC (CM) ^2 + 0.026556 FGC(CM) ^2
MVC (KG, SIT, ND)= 29.721 - 0.005548 X1^2 + 0.00098398
WEIGHT (M) ^2 + 1.9134 HEIGHT (M) ^2 - 0.015754 BMI^2 0.00083215 HGC (CM) ^2 + 0.029131FGC(CM) ^2
MVC (KG, STAND, D)= 69.781 - 0.004364X1^2 + 0.0042153
WEIGHT (M) ^2 - 12.522 HEIGHT (M) ^2 - 0.050178 BMI^2 +
0.0014772 HGC (CM) ^2 + 0.036575 FGC(CM) ^2
MVC (KG, STAND, ND)= 61.015 - 0.0054228 X1^2 + 0.0032202
WEIGHT(M) ^2 - 10.816 HEIGHT (M) ^2 - 0.032784 BMI^2 +
0.0095812 HGC (CM) ^2 + 0.027222 FGC(CM) ^2
Errors
RMSE: 5.84
R-SQ: 0.516
RMSE: 6.2
R-SQ: 0.466,
RMSE: 5.69
R-SQ: 0.554,
RMSE: 6.78
R-SQ: 0.412
It is very important to consider all variables and conditions of experiments in comparing
different models since there is no standardized procedure for all experiments (e.g., due to
different apparatuses, subject conditions, loads, etc.). Experimental research intended to
provide a wide range of choices. Some examples of other research outputs are shown in
Figure 4-2 (Chatterjee and Chowdhuri, 1991).
Figure 4-2 MVC Models (Chatterjee & Chowdhuri, 1991)
RMSEs in linear and nonlinear regression models were compered. Table 4-11 shows the
comparison. As shown in Table 4-12, the RMSE (6.125) of the nonlinear model is almost
82
the same as the linear model, i.e., the nonlinear models result in limited improvements
from the linear models, with almost same R-SQ values for both methods.
Table 4–11 RMSE Values for Linear and Nonlinear Regression Models
RMSE Linear
Regression
RMSE Non Linear
Regression
R-SQ linear
R-SQ Nonlinear
Sitting Posture (Avg)
6.01
6.02
0.49
0.49
Standing Posture (Avg)
6.25
6.23
0.47
0.52
Dominant Hand (Avg)
5.81
5.76
0.52
0.44
Non Dominant Hand
(Avg)
Avg
6.445
6.49
0.44
0.48
6.12875
6.125
0.48
0.4825
In particular, significant factors are extracted for each posture. Regression equations are
provided for both general linear regression and stepwise linear regression equations for
all factors and their interactions. Residual plots support normality assumption, where
only one case was not normal in the isometric calculations. It was normalized by using
cox–box transformation.
In the following paragraph, we examine individual factors in detail. The individual
factors include posture (standing and sitting), age, FAC, GC, smoking status, hand
dominancy, race, and BMI.
Posture (standing and sitting) effect: There is limited literature about posture effect.
Most of aviation work involves standing positions similar to other general trades (such as
fire men, police, and athletics, etc.). Ibarra et al. (2012) mentioned that subjects exerted
more grip strength in the standing position than the sitting position by 3%. They found
that for pinch grip no statistical difference was found between standing and sitting
positions; but key pinch strength was marginally higher for standing and sitting positions.
83
Table 4-12 shows mean MVC values for different age groups with standing and sitting
posture.
Table 4-12 MVC Values for Posture (Standing and Sitting)
MVC (Avg;
Sitting; DH)
MVC (Avg;
Sitting;
NDH)
MVC (Avg;
Standing; DH)
MVC (Avg;
Standing;
NDH)
Avg
A1: (30- <35)
49.26
49.94
50.8
49.04
49.76
A0: (25-<30)
46.63
50.76
49.98
48.04
48.85
A3: (40-<45)
49.73
46.52
51.43
46.3
48.50
A2: (35-<40)
49.11
46.8
49.17
45.44
47.63
A4: (45-<50)
45.58
44.79
47.49
43.79
45.41
39.18
36.8
40.73
36.99
38.43
46.58
45.94
48.27
44.93
46.43
Age Group
A5: (Above
50)
Avg
The sitting posture average (46.258 kg) is almost the same as the standing posture
average (46.60 kg). The percentage for both hands in standing/sitting is 1.0073 (0.07%
more); that for the dominant hand in standing/sitting is 1.036 (3.61% more); that for the
dominant hand in standing/sitting is 0.97 (2.02% less). Results agreed with findings of
Ibarra et al. (2012): Subjects exerted more grip strength in the standing position than the
sitting position by 3%. Figures 4-3 and 4-4 show the results for both hands.
84
90
80
Max (MVC)Kg
Max (MVC)Kg
Sitting
Right
Standing Right
70
60
50
40
30
20
10
1
5
9
13
17
21
25
29
33
37
41
45
49
53
57
61
65
69
73
77
81
85
89
93
97
101
105
109
113
117
121
125
129
0
Figure 4-3 MVC Posture Effect (D)
90
80
Max (MVC)Kg
Sitting
Max (MVC)Kg
Standing
Left
Left
70
60
50
40
30
20
10
1
5
9
13
17
21
25
29
33
37
41
45
49
53
57
61
65
69
73
77
81
85
89
93
97
101
105
109
113
117
121
125
129
0
Figure 4-4 MVC Posture effect (ND)
The general linear equations for MVC with posture effect are as follows:
SIT, MVC (Kg) = -21.72 - 0.4349 Age (Y) + 22.07 Height (M) - 0.362 BMI
+ 0.142 HGC (CM) + 1.844 FAC (CM
STA, MVC (Kg) = -21.47 - 0.4349 Age (Y) + 22.07 Height (M) - 0.362 BMI
+ 0.142 HGC (CM) + 1.844 FAC (CM)
85
The following paragraphs discuss effects of individual factors including age, trade,
smoking, BMI, hand grip circumference, dominancy, forearm circumference, posture,
and height on MVC.
Age effect: There was a limited number of studies that covered the age groups of 25-29
and 35-50 years old. Most researchers do not agree on the most significant age group,
possibly as a result of different experimental conditions. This dissertation includes age
groups with 5-year age intervals (A0: 25-<30, A1: 30-<35, A2: 35-<40, A3: 40-<45, A4:
45-<50, A5: Above 50. Chatterjee and Chodhuri (1991), Al Meanazel (2013), and
Minnal (2014) considered young ages between 18 and 25 years old. Koley et al. (2009)
considered middle ages between 18 and 40 years old. Bohannon et al. (2006) considered
old ages (75-79, 80-84, 85-89 and 90-99 years old). In all cases, categorizing ages into 5year intervals is desirable. Table 4-3 shows the independent variables (e.g., age) with the
six levels, their total counts, mean, and standard deviation. Analysis on MVC finds the
age effect. Asmussen and Heeboll-Nielsen (1955, 1956, and 1962) stated that ages
around 20 years old have a peak MVC amount, which then started to decline with older
ages. Chatterjee and Chodhuri (1991) mentioned that maximum MVC can be achieved by
subjects who are 18 to 22 years old. Anakwe et al. (1995) studied subjects aged 35 to 44
years old. Many other researchers mentioned that MVC is independent of age including
Petrofsky and Linda (1975), which found no effect of aging on isometric muscle strength.
Bohannon et al. (2006) found grip strength inversely proportional with ages. Table 4-14
shows descriptive statistics for the age intervals and maximum MVC exerted by each
interval regarding posture and dominancy. Table 4-13 shows the MVC values for
strongest age groups. Figure 4-5 shows the age effect on MVC.
86
Table 4–13 MVC Values for Strongest Age Groups
Highest MVC
Value
MVC (KG)
(SIT, D)
MVC (KG)
(SIT, ND)
MVC (KG)
(STAND,
D)
MVC (KG)
(STAND,
ND)
Lowest MVC Value
(Kg)
Similar
A3: (40-<45)
A1: (30-<35)
A2: (35-<40)
A0: (25-<30)
A1: (30-<35)
Avg (kg)
A3 (40-<45): 49.73
A5 (Above 50): 39.18
46.58
A0 (25-<30): 50.76
A5 (Above 50): 36.18
A3 (40-<45):51.43
A5 (Above 50): 40.73
A3: (40-<45)
A1: (30-<35)
48.26
A1 (30- <35):49.04
A5 (Above 50): 36.99
A1: (30-<35)
A0: (25-<30)
44.93
45.93
60
50
MVC (KG)
40
(Sitting,D)
30
(Sitting,(ND)
(Standing,(D)
20
(Standing,ND)
10
0
A5
A4
A2
A3
A0
A1
AGE PERIOD
Figure 4-5 Relationship between MVC and Age for Different Posture and Hand
Dominancy
87
60
50
A1
MVC (Kg)
40
A0
30
A3
A2
20
A4
10
A5
Avg
0
Avg
A5
A4
A2
A3
A0
A1
Age(Period)
Figure 4-6 Relationship between MVC and Age
This dissertation found that the most significant age period is A1 (30-<35) followed by
A0 (25-<30) with the weakest being A5 (Above 50) as shown in Figure 4-6. The general
linear equations for MVC with age effect are as follows:
A0, MVC (Kg) = -14.62 - 0.723 Age (Y) + 24.43 Height (M) - 0.329 BMI
(CM) + 1.742 FAC (CM)
A1, MVC (Kg) = -10.31 - 0.723 Age (Y) + 24.43 Height (M) - 0.329 BMI
(CM) + 1.742 FAC (CM)
A2, MVC (Kg) = -8.84 - 0.723 Age (Y) + 24.43 Height (M) - 0.329 BMI
(CM) + 1.742 FAC (CM)
A3, MVC (Kg) = -6.52 - 0.723 Age (Y) + 24.43 Height (M) - 0.329 BMI
(CM) + 1.742 FAC (CM)
A4, MVC (Kg) = -5.50 - 0.723 Age (Y) + 24.43 Height (M) - 0.329 BMI
(CM) + 1.742 FAC (CM)
A5, MVC (Kg) = -4.5 - 0.723 Age (Y) + 24.43 Height (M) - 0.329 BMI
(CM) + 1.742 FAC (CM)
- 0.040 HGC
- 0.040 HGC
- 0.040 HGC
- 0.040 HGC
- 0.040 HGC
- 0.040 HGC
Height effect: Alex et al. (2013) stated that “a positive association was documented
between height and dominant hand grip strength, while the respective comparison for
weight and dominant hand strength documented a statistically significant positive
association only in the male group”. . Figure 4-7 shows MVC versus height relationships
88
60
50
MVC (Kg)
40
Tall
30
Meduim
20
Short
10
0
(Sit,D)
(Sit,ND)
(Stand,D)
(Stand,ND)
Height Group
Figure 4-7 Relationship between MVC and Height
Research found that height has a major effect on MVC where taller people exerted more
MVC with additional 9.1% than medium, and 12.21% than short subjects. The general
linear equations for MVC with height effect are as follows:
S, MVC (Kg) = -37.8 - 0.4342 Age (Y) + 31.57 Height (M) - 0.381 BMI + 0.148 HGC
(CM) + 1.873 FAC (CM)
M, MVC (Kg) = -38.7 - 0.4342 Age (Y) + 31.57 Height (M) - 0.381 BMI + 0.148 HGC
(CM) + 1.873 FAC (CM)
T, MVC (Kg) = -40.1 - 0.4342 Age (Y) + 31.57 Height (M) - 0.381 BMI + 0.148 HGC
(CM) + 1.873 FAC (CM)
BMI effect: Sheriff et al., (2012); Montes (2001); Minnal (2014); Al Meanazel (2013),
and Stulen and De Luca (1981) mentioned that MVC depends on muscle strength and
brain-related factors. Montes (2001) stated that higher muscle diameters were noted in
the maximum voluntary isometric contraction. Stulen and De Luca (1981) mentioned that
MVC value depends on both muscle strength and brain-related factors, different levels of
89
grip strength holding time do not vary between weaker and stronger individuals when
subjected to the same load. This might be due to loads used being small or the condition
of experiment itself. Figure 4-8 shows MVC versus BMI relationships.
52
50
MVC (Kg)
48
(Sit,D)
46
(Sit,ND)
(Stand,D)
44
(Stand,ND)
42
40
large
Meduim
Short
BMI
Figure 4-8 Relationship between MVC and BMI
This research showed that greater MVC is exerted by subjects with medium BMI, and
highest MVC is exerted in MVC (Kg, Stand, D) condition. In all cases, we do not find
relation between strong or weak muscles and BMI. Results show being the medium BMI
is most likely to have better MVC values that being in other categories. The general
linear equations for MVC with BMI effect are as follows:
L, MVC (Kg) = -20.03 - 0.4386 Age (Y) + 21.31 Height (M) - 0.432 BMI + 0.264 HGC
(CM) + 1.803 FAC (CM)
M, MVC (Kg) = -19.07 - 0.4386 Age (Y) + 21.31 Height (M) - 0.432 BMI + 0.264 HGC
(CM) + 1.803 FAC (CM)
90
S, MVC (Kg) = -20.52 - 0.4386 Age (Y) + 21.31 Height (M) - 0.432 BMI + 0.264 HGC
(CM) + 1.803 FAC (CM)
Hand grip circumference (HGC) effect: This research found that there is a strong
correlation between hand volume and maximum MVC. Minnal (2014) and Al Meanazel
(2013) mentioned that higher grip circumference exerted more MVC. Other researchers
obtained different values for MVC (lower or higher) and similar for all other percentages.
Figure 4-8 shows the relationship between MVC and HGC. This research found that
subjects exerted more MVC when they have larger HGC, and the highest MVC is exerted
in MVC (Kg, Stan, D) condition. The general linear equations for MVC with HGC effect
are as follows:
L, MVC (Kg) = 15.0 - 0.4379 Age (Y) + 24.16 Height (M) - 0.349 BMI - 1.343 HGC
(CM) + 1.727 FAC (CM)
M, MVC (Kg) = 11.1 - 0.4379 Age (Y) + 24.16 Height (M) - 0.349 BMI - 1.343 HGC
(CM) + 1.727 FAC (CM)
S, MVC (Kg) = 8.6 - 0.4379 Age (Y) + 24.16 Height (M) - 0.349 BMI - 1.343 HGC
(CM) + 1.727 FAC (CM)
Forearm grip circumference (FAC) effect: There were a very limited number of
studies focusing on forearm circumference for both dominant and non-dominant hands.
Anakwe et al. (2007) stated that “Forearm circumference generally decreased with age
for both men and women, although this decline was less marked for women”. Fraser et al.
(1999) also mentioned that “British subjects have slightly greater values for dominant
forearm circumference measurements in both men and women (29.1 cm Vs 24.3 cm for
men and 25.6 cm vs. 20.4 cm for women”. Kallman et al. (1990) found that forearm
circumference delivered the best practical method for assessing the MVC grip strength
91
and muscle mass for both genders. Figure 4- 9 shows the relationship between MVC and
FAC.
60
50
MVC(kg)
40
large
30
Meduim
20
Small
10
0
(Sit,D)
(Sit,ND)
(Stand,D)
(Stand,ND)
Posture & Dominancy
Figure 4-9 Relationship between FAC and MVC
The general linear equations for isometric for MVC with FAC effect are as follows:
L, MVC (Kg) = -27.9 - 0.4366 Age (Y) + 22.05 Height (M) - 0.354 BMI + 0.135 HGC
(CM) + 2.036 FAC (CM)
M, MVC (Kg) = -27.3 - 0.4366 Age (Y) + 22.05 Height (M) - 0.354 BMI + 0.135 HGC
(CM) + 2.036 FAC (CM)
S, MVC (Kg) = -26.33 - 0.4366 Age (Y) + 22.05 Height (M) - 0.354 BMI + 0.135 HGC
(CM) + 2.036 FAC (CM).
Trade effect: There was no literature taking in consideration the effect of different trades
on MVC. This dissertation examined the trade effect on MVC for five jobs in aviation
trade:
APG:
Airplane
Genera,
E&I:
Electrical
and
Instrument,
COMNAV:
Communication & Navigation, Eng: Engine and GSE: Ground Support Equipment. It
also tested smoking effect with two levels (smokers and non-smokers). Figure 4-10
shows the relationship between MVC and trades.
92
60
50
MVC(kg)
40
(Sit,D)
(Kg,Sit,ND)
30
(Stand,D)
(Stand,ND)
20
10
0
APG
COMNAV
E&I
ENG
GSE
Trade
Figure 4-10 Relationship between Trade and MVC for Different Posture and
Dominancy
This research found that all trades mostly exerted the same MVC; however, the highest
MVC was exerted by Eng and E & I trades which is 37 for mean ages of 42 year old (the
most significant age range) and the highest MVC is exerted in MVC (Kg, Stand, D)
condition. The general linear equations for isometric for MVC with trade effect are as
follows:
APG: MVC (Kg) = -24.66 - 0.4309 Age (Y) + 20.40 Height (M)
+ 0.465 HGC (CM) + 1.892 FAC (CM)
COMNAV: MVC (Kg) = -24.94 - 0.4309 Age (Y) + 20.40 Height (M)
+ 0.465 HGC (CM) + 1.892 FAC (CM)
E&I: MVC (Kg) = -25.56 - 0.4309 Age (Y) + 20.40 Height (M)
+ 0.465 HGC (CM) + 1.892 FAC (CM)
ENG: MVC (Kg) = -26.66 - 0.4309 Age (Y) + 20.40 Height (M)
+ 0.465 HGC (CM) + 1.892 FAC (CM)
GSE: MVC (Kg) = -26.03 - 0.4309 Age (Y) + 20.40 Height (M)
+ 0.465 HGC (CM) + 1.892 FAC (CM)
93
- 0.437 BMI
- 0.437 BMI
- 0.437 BMI
- 0.437 BMI
- 0.437 BMI
Race effect: Tables 4-14, 4-15 provides descriptive statistics and summary of MVC
values for different races. Figures 4-11 shows MVC Vs race relationships. Those values
cannot be used for comprehensive comparisons, since more information about
experimental subjects and anthropometric data are needed in all cases. The effect of race
on MVC depends on many factors, such as culture (especially for women), physical
factors, age, etc.
Table 4-14 Descriptive Statistics for Jordanian Subjects
Variable
Mean
StDev
Minimum
Maximum
Age (Y)
41.712
7.833
25.000
65.000
Weight (Kg )
82.60
12.85
55.00
114.00
Height (M)
1.7581
0.0705
1.5500
1.9300
BMI
26.679
3.600
18.711
37.422
HGC(CM
22.523
1.338
19.500
25.500
FAC (CM)
29.341
2.441
23.000
35.00
Table 4-15 Descriptive Statistics: MVC Values for Different Races
Population
MVC (Kg)
(Males)
MVC (Kg)
(Females)
Author(s) (Year)
Singaporean
24.1
N/A
Incel et al. (2002)
Indian
30-39.8
22.75
Jordan (Pilot Study)
33.619
N/A
Vaz et al. (1998, 2002), Koley et al.
(2009)
Al-Momani (2015)
Spanish
39.95
25.72
Heredia et al. (2005)
Scotland
35.12
23.02
Heredia et al. (2005)
Scotland
40.0–48.8
27.5–34.4
Brenner et al. (1989)
Jordan
46.58167
N/A
Al-Momani (2015)
USA
62.0
37.0
Crosby and Wehbe (1994)
USA
44.8
35.0
Al Meanazel (2013)
94
70
60
MVC9Kg)
50
40
30
20
MVC (Kg) (Males)
10
0
USA
USA
Jordan
Scotland
Scotland
Spanish
Jordan (Pilot Study)
Indian
Singaporean
Race
Figure 4-11 Relationship between MVC and Race (Male)
The subject group (from 25 to 60 years old) showed the following means: age (41.71
years old), weight (82.6 Kg), height (1.75 m), BMI (26.67), hand grip circumference
(22.52 cm), forearm circumference (29.34 cm), and MVC (46.58 kg). In general, the race
factor is very important since it is related to culture, life style, and physical factors in
general. Most studies focusing on the relationship between MVC and race were
performed in USA and UK, whereas a very limited number of studies was found for the
race effect worldwide.
Smoking Effect: Most researchers such as Isaac and Rand (1969) said that smoking
leads to profound vasoconstriction, results in tissues starving of nutritive blood and
bypassing from arterioles to venules. Asano and Branemark (1970) said that nicotine
levels increase in plasma up to 10 micrograms per 100 mm of blood. Sorensen et al.
(2009) mentioned that smoking workers’ capabilities decreased because of lung
incapacity to provide more oxygen to muscles. Asano and Branemark (1970), Isaac and
95
Rand (1969); Davis (1960) and Al Meanazel (2013) found that non-smokers can exert
more force. Figure 4-12 shows the relationship between MVC and smoking.
120
100
MVC (kg)
80
60
S
40
NS
20
0
(Sit,D)
(Sit,ND)
(Stand,D)
(Stand,ND)
Smoking (Posture & Dominancy)
Figure 4-12 Relationship between MVC and Smoking
This research found that smokers exerted more MVCs in sitting than those in standing by
2%, and exerted more MVC using the dominant hand by extra 5.52% for the strongest
age group. Also, highest MVC was exerted in MVC (Kg, Stand, D) condition. All
researchers connected smoking with lower performance in all aspects. This research
finding disagreed with most researchers and might be related to nature of the experiment
and age of smoking subjects as well as the fact that two thirds of all subjects are smokers.
The general linear equations for MVC with smoking effect are as follows:
NS, MVC (Kg) = -20.94 - 0.4369 Age (Y) + 21.66 Height (M) - 0.379 BMI + 0.142 HGC
(CM) + 1.875 FAC (CM)
S, MVC (Kg) = -21.48 - 0.4369 Age (Y) + 21.66 Height (M) - 0.379 BMI + 0.142 HGC
(CM) + 1.875 FAC (CM)
96
Dominancy effect: Dominancy has been classified into two levels (dominant and nondominant). Armstrong and Oldham (1999) stated that “dominant hand is significantly
stronger than non-dominant hand”. Ibarra et al. (2012) stated that the dominant hand is
stronger by 0.1–3% than the non-dominant hand in right-handed people and very little
difference in hand dominancy is found in left-handed people. Incel et al. (2002) and
Bohannon et al. (2006) mentioned that the dominant hand is stronger by 3.9%. Bohannon
et al. (2006), Koley et al. (2009), Sorensen et al. (2009), and Al Meanazel (2013) agreed
that the dominant hand is stronger by 10.9% and 33.3% for both hands, and the dominant
right hand is stronger than the dominant left hand. But other researchers such as Incel et
al. (2002) stated that “there is no difference between dominant and non-dominant hand”
on MVC values and found that no difference in grip strength between left- and righthanded persons. Figure 4-13 shows the relationship between MVC and dominancy. There
were 122 subjects with the dominant hand being the right hand and 10 subjects being lefthand dominant.
60
MVC (KG)
50
40
(Sitting),(DH)
30
(Sitting),(NDH)
20
(Standing),(DH)
10
(Standing),(NDH)
0
A5
A4
A2
A3
A0
A1
HAND DOMINANCY EFFECT
Figure 4-13 Relationship between Hand Dominancy and MVC for Different Age
Groups, Hand Dominancy, and Posture
Research results showed that:
1- In general, the dominant hand exerted more MVC than the non-dominant hand for
all age groups. Max MVC in sitting (D) is 46.58 kg; Max MVC in standing (D) is
97
48.26 kg; Max MVC in sitting (ND) is 45.93 kg; and Max MVC in standing (ND)
is 44.93 kg.
2- Dominant hand in standing posture exerts more MVC than that in sitting posture
by 3.6%.
3- Non-dominant hand in sitting posture exerts more MVC than that in standing
posture by 2.2%.
4- Dominant hand in sitting posture exerts more MVC than non-dominant hand by
1.4%.
5- Dominant hand in standing posture exerts more MVC than non-dominant hand
by 7.41%
6- Other factors do not have significant effects on MVC.
7- The highest MVC was exerted by the dominant hand of subjects aged 30-45 years
old, followed by those aged 25-30 years old and above 45 years old.
8- For the non-dominant hand, the younger subjects (25-30 years old) exerted most
MVC, followed by those aged 30-35 and above 35 years old. The reason might be
that at younger ages both hands have almost the same strength; however, as
getting older, the subjects use the dominant hands more frequently which become
stronger. The general linear equations for isometric for MVC with dominancy
effect are as follows:
D, MVC (Kg) = -22.69 - 0.4302 Age (Y) + 22.60 Height (M) - 0.352 BMI + 0.174 HGC
(CM) + 1.813 FAC (CM)
ND, MVC (Kg) = -24.42 - 0.4302 Age (Y) + 22.60 Height (M) - 0.352 BMI
+ 0.174 HGC (CM) + 1.813 FAC (CM).
98
4.6 Isometric Endurance Limit: Analysis and Discussion
During the last decades, studies on MVC isometric and isotonic endurance limits used
different fractions of MVC (5%, 10%, 15%, 20%, 30%, 40%, 60%, 80%). There were no
experimental standardizations which make it difficult to compare. The fractions of 25%,
50%, and 75% of the MVC were tested, which were changed to 20%, 40%, 60%, and
80% for comparisons with latest studies during the last five years. ANOVA results are
presented in this section, in addition to the predicted general linear and nonlinear models
for isometric endurance limit. Table 4-16 shows the dependent factors, and independent
variables with their levels. Tables 4-17, 4-18, 4-19 and 4-20 show the general linear
equations for isometric endurance limit with age effect.
Table 4-16 Factor Information for ANOVA General Factorial Regression
Dependent Variables
Treatment Levels
Independent
Variables
1- MVC
2- Isometric Endurance
Limit (20%, 40%, 60%,
80%)
3- Isotonic Endurance
limit (20-60%)
Fixed Factors
Age (years)
1)
2)
3)
4)
5)
6)
Trade
1) APG: Airplane General
2) E&I: Electrical and
Instrument
3) COMNAV: Communication
& Navigation
4) Eng: Engine
5) GSE: Ground Support
Equipment
Smoking
1) Smokers
2) Non-smokers
3- Jordanian Subjects
4- Digital Dynamometer
99
A0: (25-<30)
A1: (30-<35)
A2: (35-<40)
A3: (40-<45)
A4: (45-<50)
A5: Above 50
Body Mass Index
(BMI)
1) Small: S (19-<25)
2) Medium: M (25-<30)
3) Large: L (Above 30)
Hand Grip
Circumference (CM)
4) Small: S (=< 21.5)
5) Medium: M (21.5-<23.5)
6) Large: L (Above 23.5)
Hand Dominancy
1) D: Dominant
2) ND: Non-Dominant
Forearm
Circumference (CM)
1) Small: S (<= 27.5)
2) Medium: M (>27.5-31)
3) Large: L (Above 31)
Posture
1) Sitting: SIT
2) Standing: STD
Height (M)
1) Short: S (<= 1.70)
2) Medium: M (1.70-<1.81)
3) Tall: T (Above 1.81)
100
Table 4-17 ANOVA General Factorial Regression: Isometric En 20%
Source
DF
Adj SS
Adj MS
F-Value
P-Value
Model
Linear
Posture
Age (Cat)
Hand Dominancy (HD)
Trade
Smoking
Height(Cat)
BMI (Cat)
HGC (Cat)
FAC(Cat)
2-Way Interactions
Posture*Age (Cat)
Posture*H D
Posture*Trade
Posture*Smoking
Posture*Height ( Cat)
Posture*BMI (Cat)
Posture*HGC (Cat)
Posture*FAC (Cat)
Age (Cat)*Smoking
H D*Smoking
H D*BMI (Cat)
H D*HGC (Cat)
H D*FAC (Cat)
Trade*Smoking
Trade*Height (Cat)
Trade*BMI (Cat)
Smoking*Height (Cat)
Smoking*BMI (Cat)
Smoking*HGC (Cat)
Smoking*FAC (Cat)
Height (CAT)*BMI
(Cat)
Height (CAT)*HGC
(Cat)
Height (Cat)*FAC (Cat)
BMI (Cat)*HGC (Cat)
Error
Lack-of-Fit
Pure Error
Total
95
20
1
5
1
4
1
2
2
2
2
75
5
1
4
1
2
2
2
2
5
1
2
2
2
4
8
8
2
2
2
2
4
1775.90
552.01
0.00
79.38
81.75
177.74
6.31
50.47
13.20
18.68
67.18
1095.97
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
23.26
4.07
36.41
93.33
55.98
33.88
86.50
54.08
10.93
17.23
2.60
10.87
50.07
18.6937
27.6005
0.0000
15.8756
81.7499
44.4352
6.3086
25.2370
6.6024
9.3384
33.5908
14.6129
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
4.6527
4.0691
18.2043
46.6663
27.9919
8.4699
10.8122
6.7599
5.4672
8.6145
1.3003
5.4357
12.5164
6.67
9.85
0.00
5.66
29.16
15.85
2.25
9.00
2.36
3.33
11.98
5.21
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.66
1.45
6.49
16.65
9.99
3.02
3.86
2.41
1.95
3.07
0.46
1.94
4.47
0.000
0.000
1
0.000
0,000
0.000
0.134
0.000
0.096
0.007
0.000
0.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
0.143
0.229
0.002
0.000
0.000
0.018
0.000
0.015
0.143
0.047
0.629
0.145
0.002
4
73.01
18.2528
6.51
0.000
4
4
432
150
282
527
75.88
201.16
1210.90
1044.17
166.74
2986.80
18.9691
50.2899
2.8030
11.77
0.5913
6.77
17.94
0.000
0.000
101
6.9611
Table 4-18 ANOVA General Factorial Regression: Isometric En 40%
Source
DF
Adj SS
Adj MS
F-Value
P-Value
Model
Linear
Posture
Age (Cat)
HD
Trade
Smoking
Height( Cat)
BMI (Cat)
HGC (Cat)
FAC (Cat)
2-Way Interactions
Posture*Age (Cat)
Posture*H D
Posture*Trade
Posture*Smoking
Posture*Height (Cat)
Posture*BMI (Cat)
Posture*HGC (Cat)
Posture*FAC (Cat)
Age (Cat)*Smoking
H D*Smoking
H D*BMI(Cat)
H D*HGC (Cat)
H D*FAC(Cat)
Trade*Smoking
Trade*Height(Cat)
Trade*BMI(Cat)
Smoking*Height(Cat)
Smoking*BMI (Cat)
Smoking*HGC (Cat)
Smoking*FAC (Cat)
Height (Cat)*BMI (Cat)
Height (Cat)*HGC (Cat)
Height (Cat)*FAC(Cat)
BMI (Cat)*HGC (Cat)
Error
Lack-of-Fit
Pure Error
Total
95
20
1
5
1
4
1
2
2
2
2
75
5
1
4
1
2
2
2
2
5
1
2
2
2
4
8
8
2
2
2
2
4
4
4
4
432
150
282
527
81.750
23.414
0.000
4.945
0.705
2.188
4.509
1.473
0.045
2.463
2.375
38.674
0.000
0.000
0.000
0.000
0.000
0.000
0.00
0.00
0.913
2.942
1.194
0.204
2.056
4.249
3.085
3.274
0.665
1.988
0.161
0.200
6.801
1.698
4.320
2.570
49.834
44.457
5.378
527 131.585
0.86053
1.17068
0.00000
0.98908
0.70485
0.54694
4.50856
0.73673
0.02271
1.23149
1.18731
0.51566
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.000
0.000
0.18262
2.94158
0.59700
0.10207
1.02809
1.06219
0.38565
0.40926
0.33266
0.99401
0.08053
0.09998
1.70037
0.42454
1.07996
0.64254
0.11536
0.29638
0.01907
7.46
10.15
0.00
8.57
6.11
4.74
39.08
6.39
0.20
10.68
10.29
4.47
0.00
0.00
0.00
0.00
0.00
0.00
0.00000
0.00000
1.58
25.50
5.18
0.88
8.91
9.21
3.34
3.55
2.88
8.62
0.70
0.87
14.74
3.68
9.36
5.57
0.000
0.000
1.000
0.000
0.014
0.001
0.000
0.002
0.821
0.000
0.000
0.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
0.164
0.000
0.006
0.414
0.000
0.000
0.001
0.001
0.057
0.000
0.498
0.421
0.000
0.006
0.000
0.000
15.54
0.000
102
Table 4-19 ANOVA General Factorial Regression: Isometric En 60%
Source
DF
DF
Adj SS
Adj MS
F-Value
P-Value
Model
Linear
Posture
Age (Cat)
HD
Trade
Smoking
Height (Cat)
BMI (Cat)
HGC (Cat)
FAC (Cat)
2-Way Interactions
Posture*Age (Cat)
Posture*H D
Posture*Trade
Posture*Smoking
Posture*Height (Cat)
Posture*BMI (Cat)
Posture*HGC (Cat)
Posture*FAC (Cat)
Age (Cat)*Smoking
H D*Smoking
H D*BMI (Cat)
H D*HGC (Cat)
H D*FAC (Cat)
Trade*Smoking
Trade* Height (Cat)
95
20
1
5
1
4
1
2
2
2
2
75
5
1
4
1
2
2
2
2
5
1
2
2
2
4
8
95
20
1
5
1
4
1
2
2
2
2
75
5
1
4
1
2
2
2
2
5
1
2
2
2
4
8
99.224
21.385
0.000
2.987
1.651
2.394
5.254
0.954
0.703
0.876
2.481
64.169
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
10.355
5.166
1.055
0.050
3.489
5.241
1.746
1.04447
1.06927
0.00000
0.59732
1.65103
0.59849
5.25394
0.47688
0.35143
0.43786
1.24047
0.85559
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
2.07101
5.16588
0.52767
0.02504
1.74446
1.31027
0.21822
7.23
7.40
0.00
4.13
11.43
4.14
36.37
3.30
2.43
3.03
8.59
5.92
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
14.34
35.76
3.65
0.17
12.07
9.07
1.51
0.000
0.000
1.000
0.001
0.001
0.003
0.000
0.038
0.089
0.049
0.000
0.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
0.000
0.000
0.027
0.841
0.000
0.000
0.151
Trade*BMI (Cat)
Smoking* Height (Cat)
Smoking*BMI (Cat)
Smoking*HGC (Cat)
Smoking*FAC (Cat)
Height(Cat)*BMI(Cat)
Height (Cat)*HGC (Cat)
Height (Cat)*FAC (Cat)
BMI (Cat)*HGC (Cat)
Error
8
2
2
2
2
4
4
4
4
432
8
2
2
2
2
4
4
4
4
432
4.146
1.215
0.461
0.488
0.070
8.722
1.149
1.535
7.372
62.411
0.51828
0.60774
0.23031
0.24401
0.03501
2.18058
0.28715
0.38386
1.84292
0.14447
3.59
4.21
1.59
1.69
0.24
15.09
1.99
2.66
12.76
0.000
0.016
0.204
0.186
0.785
0.000
0.095
0.032
0.000
Lack-of-Fit
Pure Error
150
282
150
282
56.831
5.580
0.37888
0.01979
19.15
0.000
Total
527
527
161.635
103
Table 4-20 ANOVA General Factorial Regression: Isometric En 80%
Source
DF
DF
Adj SS
Adj MS
F-Value
P-Value
Model
Linear
Posture
Age (Cat)
HD
Trade
Smoking
95
20
1
5
1
4
1
2
2
2
2
75
5
1
4
1
2
2
2
2
5
1
2
2
2
4
8
8
2
2
2
2
4
4
4
4
432
150
282
527
95
20
1
5
1
4
1
2
2
2
2
75
5
1
4
1
2
2
2
2
5
1
2
2
2
4
8
8
2
2
2
2
4
4
4
4
432
150
282
527
1.30229
0.24234
0.00000
0.03101
0.01802
0.06961
0.03737
0.01194
0.00331
0.03104
0.01052
0.73273
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.10127
0.03778
0.01071
0.00104
0.02276
0.08695
0.06235
0.06225
0.03354
0.00820
0.00639
0.00444
0.07743
0.02671
0.01718
0.08177
0.64677
0.59516
0.05161
1.94906
0.013708
0.012117
0.000000
0.006203
0.018019
0.017402
0.037370
0.005968
0.001655
0.015522
0.005258
0.009770
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.020254
0.037778
0.005353
0.000518
0.011381
0.021738
0.007794
0.007781
0.016769
0.004100
0.003197
0.002219
0.019358
0.006677
0.004296
0.020443
0.001497
0.003968
0.000183
9.16
08.09
0.00
4.14
12.04
11.62
24.96
3.99
1.11
10.37
3.51
6.53
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
13.53
25.23
3.58
0.35
7.60
14.52
5.21
5.20
11.20
2.74
2.14
1.48
12.93
4.46
2.87
13.65
0.000
0.000
1.000
0.001
0.001
0.000
0.000
0.019
0.332
0.000
0.031
0.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
0.000
0.000
0.029
0.708
0.001
0.000
0.000
0.000
0.000
0.066
0.119
0.228
0.000
0.002
0.023
0.000
21.68
0.000
Height(Cat)
BMI (Cat)
HGC (Cat)
FAC (Cat)
2-Way Interactions
Posture*Age (Cat)
Posture*H D
Posture*Trade
Posture*Smoking
Posture*Height (CAT)
Posture*BMI (Cat)
Posture*HGC(Cat)
Posture*FAC (Cat)
Age (Cat)*Smoking
H D*Smoking
H D*BMI (Cat)
H D*HGC (Cat)
H D*FAC (Cat)
Trade*Smoking
Trade*Height (Cat)
Trade*BMI (Cat)
Smoking* Height(Cat)
Smoking*BMI (Cat)
Smoking*HGC (Cat)
Smoking*FAC (Cat)
Height (Cat)*BMI (Cat)
Height (Cat)*HGC (Cat)
Height (Cat)*FAC (Cat)
BMI (Cat)*HGC (Cat)
Error
Lack-of-Fit
Pure Error
Total
Tables 4-21 and 4-22 show the significant and non-significance factors with the twofactor interactions; but as mentioned before, the experiment is a human social
104
experiment. Many studies found the above non-significant factors to be significant.
Linear regression equations were derived for all independent factors.
Table 4-21 Significant Factors Found with ANOVA
Significant Factors
Non-significant
Factors
Model
Summary
Isometric En
20%
AGE, HD, Trade, Height,
HGC, FAC
Smoking BMI
Posture
S :1.67
R-sq : 59.46%
Isometric En
40%
AGE, HD, Trade, Height,
HGC, FAC, Smoking
BMI Posture
S :0.339
R-sq : 62.13%
Isometric En
60%
AGE, Posture, HD, Trade,
HGC, FAC, Smoking
BMI, Height
S :0.38
R-sq : 61.39%
Isometric En
80%
AGE, HD, Trade, Height,
HGC, FAC, Smoking
BMI Posture
S :0.038
R-sq : 66.82%
105
Table 4-22 ANOVA Interaction Factors
Interaction Factors
H D*BMI,
Isometric
H D*HGC,
Endurance
H D*FAC,
Limit (20%)
Trade*Height(Cat),
Trade*BMI,
Smoking*Height,
Smoking*BMI,
Height*BMI,
Height*HGC,
Height*FAC, BMI*HGC
H D*FAC,
Isometric
Trade*Smoking,
Endurance
Trade*Height(Cat),
Limit (40%)
Trade*BMI,
Smoking*Height,
Smoke*BMI,
Height*BMI,
Height*HGC ,
Height*FAC, BMI*HGC
Age*Smoking,
Isometric
H D*Smoking,
Endurance
H D*BMI,
Limit (60%)
H D*FAC,
Trade*Smoking,
Trade*BMI, Trade*BMI,
Smoking*Height,
Height*BMI, BMI*HGC
Age*Smoking,
Isometric
H D*Smoking,
Endurance
H D*BMI,
Limit (80%)
H D*HGC,
H D*FAC,
Trade*Smoking,
Trade*Height( CAT),
Trade*BMI,
Smoking*Height,
Smoking*BMI,
Smoking*HGC,
Smoking*FAC, Height*BMI,
Height*HGC, Height*FAC, BMI*HGC
106
Model Summary
S: 1.67
R-sq: 59.46%
S: 0.339
R-sq: 62.13%
S :0.38
R-sq : 61.39%
S :0.038
R-sq : 66.82%
Figure 4-14 Residual plots for isometric endurance limit test
General linear and nonlinear models for isometric endurance limit at 20%, 40%, 60% and
80% of the MVC are derived by including all experimental factors in the model, using the
MATLAB 15 as shown in Tables 4-23 and 4-24. They overcome the multicollinearity
problems appeared through data analyses using MINTAB 17. The linear models will be
compared with other statistical software’s results. The isometric endurance limit test has
been conducted for the sitting position with the dominant hand to compare with others
studies.
Table 4-23 Isometric Endurance Limit General Linear Regression Models
Linear Regression Model
Errors
Isometric Endurance
Limit (20%)
127.25 + -0.0014601 * AGE(Y)^2 + -37.427
*HEIGHT(M)^2 -0.05763*BMI^2 +
0.084164*HGC(CM)^2 + 0.18183*FAC
(CM)^2
RMSE: 58.4
R-Sq: 0.116
R-Sq(Adj) 0.107
Isometric Endurance
Limit (40%)
177.76 -0.0072556* AGE(Y)^2 18.581*HEIGHT(M)^2 -0.035377*BMI^2 0.16351*HGC(CM)^2 + 0.086309*FAC
(CM)^2
RMSE: 33.5
R-Sq: 0.117
R-Sq(Adj): 0.109
107
Isometric Endurance
Limit (60%)
Isometric Endurance
Limit (80%)
Isometric Endurance
Limit (Avg)
83.723-0.0016898 b2* AGE(Y)^2 8.1407*HEIGHT(M)^2 -0.024757*BMI^2 0.088919*HGC(CM)^2 + 0.05318*FAC
(CM)^2
RMSE: 21
R-Sq: 0.0835,
R-Sq(Adj):
0.0747
44.498 + 0.0016698* AGE(Y)^2 2.8629*HEIGHT(M)^2 -0.011467*BMI^2 0.072948*HGC(CM)^2 + 0.032936*FAC
(CM)^2
108.31 -0.0021839* AGE(Y)^2
16.753*HEIGHT(M)^2 -0.032308*BMI^2 0.060302 *HGC(CM)^2 + 0.088564*FAC
(CM)^2
RMSE: 13.1
R-Sq: 0.0891,
R-Sq(Adj):
0.0804
RMSE: 23.8
R-Sq: 0.109
R-Sq(Adj): 0.101
Table 4-24 Isometric Endurance Limit Nonlinear Regression Models
Non-inear Regression Model
Errors
Isometric
Endurance
Limit (20%)
127.25 + -0.0014601 * AGE(Y)^2 -37.427
*HEIGHT(M)^2 + -0.05763*BMI^2 +
0.084164*HGC(CM)^2 + 0.18183*FAC (CM)^2
Isometric
Endurance
Limit (40%)
177.76 -0.0072556* AGE(Y)^2 18.581*HEIGHT(M)^2 -0.035377*BMI^2 0.16351*HGC(CM)^2 + 0.086309*FAC (CM)^2
Isometric
Endurance
Limit (60%)
83.723-0.0016898 b2* AGE(Y)^2 8.1407*HEIGHT(M)^2 -0.024757*BMI^2 0.088919*HGC(CM)^2 + 0.05318*FAC (CM)^2
Isometric
Endurance
Limit (80%)
44.498 + 0.0016698* AGE(Y)^2 2.8629*HEIGHT(M)^2 -0.011467*BMI^2 0.072948*HGC(CM)^2 + 0.032936*FAC (CM)^2
Isometric
Endurance
Limit (Avg)
108.31 + -0.0021839* AGE(Y)^2 6.753*HEIGHT(M)^2 -0.032308*BMI^2 0.060302*HGC(CM)^2 + 0.088564*FAC (CM)^2
RMSE: 58.4
R-Sq: 0.116
R-Sq(Adj):
0.107
RMSE: 33.5
R-Sq: 0.117
R-Sq(Adj):
0.109
RMSE: 21
R-Sq: 0.0835,
R-Sq(Adj):
0.0747
RMSE: 13.1
R-Sq: 0.0891,
R-Sq(Adj):
0.0804
RMSE: 23.8
R-Sq: 0.109
R-Sq(Adj):
0.101
108
Table 4-25 Isometric Endurance Limit RMSE Values or Linear and Nonlinear
Regression Models
Condition
Isometric Endurance Limit
(20%)
Isometric Endurance Limit
(40%)
Isometric Endurance Limit
(60%)
Isometric Endurance Limit
(80%)
Isometric Endurance Limit
(Avg)
RMSE Linear
Regression
(Matlab)
RMSE Non
Linear Regression
(Matlab)
R- Sq
linear
(Minitab)
R- Sq
nonlinear
(Minitab)
59.6
59.5
0.117
0.119
34
34.2
0.129
0.0917
21.5
21.5
0.082
0.0844
13.4
13.3
0.083
0.0945
32.1
32.1
0.102
0.0974
Table 4-26 shows RMSE Values for both linear and nonlinear regression for isometric
endurance limits. The nonlinear model results in an average RMSE of 32.1, which is
almost the same as that for the linear model, and did not result in a more accurate model.
Also, its R-squared value is around 0.0974. The following paragraphs discuss effects of
individual factors including age, trade, smoking, BMI, hand grip circumference, hand
dominancy, forearm circumference, posture, and height on isometric endurance limit.
Age effect: Age has been classified into six age intervals and this classification could
identify isometric endurance limit differences between age intervals more accurately.
Table 4-26 shows the mean of isometric endurance limit for different age groups.
According to Chatterjee and Chowdhury (1991), no effect of aging was observed on
isometric muscle strength. Yassierli et al. (2003) found that at fraction of 40% of MVC,
isometric endurance limit is independent of gender and age. Bohannon et al. (2006) found
interactive effects of different factors (gender and age) with effort level have significant
influence on fatigue, and grip strength is inversely proportional with aging. Table 4-27
109
shows means of isometric endurance limit for different age groups. Figure 4-14 shows
age effect on isometric endurance limit.
Table 4–26 Means of Isometric Endurance Limit for Different Age Groups
Age group
Isometric
Endurance
Limit (20%)
Isometric
Endurance
Limit (40%)
Isometric
Endurance
Limit (60%)
Isometric
Endurance
Limit (80%)
A0: (25-<30)
159.2
111.1
45.56
24.22
A1: (30-<35)
A3: (40-<45)
176.4
159.73
86.91
61.27
49.45
32.79
29.3
19.05
A2: (35-<40)
163.22
76.52
42.07
21.52
A4: (45-<50)
177.3
68.07
34.6
19.65
158.9
75.67
45
30.11
165.79167
79.923333
41.578333
23.975
A5: (Above
50)
Avg
Isometric
Endurance
Limit (Avg)
85.02
85.515
68.21
75.8325
74.905
77.42
77.817083
200
ISOMETRICEND, LIMIT (SEC)
180
160
140
120
20%
100
40%
80
60%
60
80%
40
20
0
A5
A4
A2
A3
A1
A0
AGE PERIOD
Figure 4-15 Relationship between Isometric Endurance Limit and Age
The mean isometric endurance limit decreases as the fraction of the MVC increases
(20%: 167.5 Seconds, 40%: 73.12 Seconds, 60%: 38.37 Seconds, 80%: 21.75 Seconds).
Two age groups (A0 (25-<30) and A1 (30-<35)) exerted the highest isometric mean
110
endurance limit (85 Seconds), followed by older ages. Results for fraction grip strength
relationship agreed with Yassierli et al. (2003) where he stated that interactive effects of
age and gender and the effort level have a significant influence on fatigue. It disagreed
with Petrofsky and Linda (1975) where no effect of aging was found in isometric muscle
strength for subjects with a very wide range of ages (between 22 and 60 years old). The
general linear equations for isometric endurance limit with age effect are as follows:
Regression Equation (20%)
A0 Isometric Endurance Limit (20%) = 39.7 + 1.57 Age (Y) - 150.1 Height (M) 3.307 BMI+ 4.59 HGC (CM) + 11.50 FAC (CM)
A1 Isometric Endurance Limit (20%) = 38.6 + 1.57 Age (Y) - 150.1 Height (M) 3.307 BMI+ 4.59 HGC (CM) + 11.50 FAC (CM)
A2 Isometric Endurance Limit (20%) = 24.2 + 1.57 Age (Y) - 150.1 Height (M) 3.307 BMI+ 4.59 HGC (CM) + 11.50 FAC (CM)
A3 Isometric Endurance Limit (20%) = 0.6 + 1.57 Age (Y) - 150.1 Height (M) 3.307 BMI + 4.59 HGC (CM) + 11.50 FAC (CM)
A4 Isometric Endurance Limit (20%) = 8.3 + 1.57 Age (Y) - 150.1 Height (M) 3.307 BMI+ 4.59 HGC (CM) + 11.50 FAC (CM)
A5 Isometric Endurance Limit (20%) = -22.9 + 1.57 Age (Y) - 150.1 Height (M) 3.307 BMI+ 4.59 HGC (CM) + 11.50 FAC (CM)
Regression Equation (40%)
A0 Isometric Endurance Limit (40%) = 300.6 - 2.580 Age (Y) - 25.4 Height (M) 1.036 BMI- 6.49 HGC (CM) + 3.117 FAC (CM)
A1 Isometric Endurance Limit (40%) = 293.6 - 2.580 Age (Y) - 25.4 Height (M) 1.036 BMI- 6.49 HGC (CM) + 3.117 FAC (CM)
A2 Isometric Endurance Limit (40%) = 285.9 - 2.580 Age (Y) - 25.4 Height (M) 1.036 BMI- 6.49 HGC (CM) + 3.117 FAC (CM)
A3 Isometric Endurance Limit (40%) = 312.1 - 2.580 Age (Y) - 25.4 Height (M) 1.036 BMI 6.49 HGC (CM) + 3.117 FAC (CM)
111
A4 Isometric Endurance Limit (40%) = 318.2 - 2.580 Age (Y) - 25.4 Height (M) 1.036 BMI- 6.49 HGC (CM) + 3.117 FAC (CM)
A5 Isometric Endurance Limit (40%) = 350.5 - 2.580 Age (Y) - 25.4 Height (M) 1.036 BMI - 6.49 HGC (CM) + 3.117 FAC (CM)
Regression Equation (60%)
A0 Isometric Endurance Limit (60%) = 144.5 - 1.771 Age (Y) + 0.1 Height (M) 0.880 BMI- 4.039 HGC (CM) + 1.976 FAC (CM)
A1 Isometric Endurance Limit (60%) = 160.8 - 1.771 Age (Y) + 0.1 Height (M) 0.880 BMI- 4.039 HGC (CM) + 1.976 FAC (CM)
A2 Isometric Endurance Limit (60%) = 155.6 - 1.771 Age (Y) + 0.1 Height (M) 0.880 BMI - 4.039 HGC (CM) + 1.976 FAC (CM)
A3 Isometric Endurance Limit (60%) = 173.0 - 1.771 Age (Y) + 0.1 Height (M) 0.880 BMI- 4.039 HGC (CM) + 1.976 FAC (CM)
A4 Isometric Endurance Limit (60%) = 175.3 - 1.771 Age (Y) + 0.1 Height (M) 0.880 BMI - 4.039 HGC (CM) + 1.976 FAC (CM)
A5 Isometric Endurance Limit (60%) = 203.4 - 1.771 Age (Y) + 0.1 Height (M) 0.880 BMI - 4.039 HGC (CM) + 1.976 FAC (CM)
Regression Equation (80%)
A0 Isometric Endurance Limit (80%) = 46.1 + 0.410 Age (Y) - 4.3 Height (M) 0.568 BMI - 2.894 HGC (CM) + 1.795 FAC (CM)
A1 Isometric Endurance Limit (80%) = 50.4 + 0.410 Age (Y) - 4.3 Height (M) 0.568 BMI - 2.894 HGC (CM) + 1.795 FAC (CM)
A2 Isometric Endurance Limit (80%) = 39.9 + 0.410 Age (Y) - 4.3 Height (M) 0.568 BMI- 2.894 HGC (CM) + 1.795 FAC (CM)
A3 Isometric Endurance Limit (80%) = 39.6 + 0.410 Age (Y) - 4.3 Height (M) 0.568 BMI- 2.894 HGC (CM) + 1.795 FAC (CM)
A4 Isometric Endurance Limit (80%) = 35.2 + 0.410 Age (Y) - 4.3 Height (M) 0.568 BMI- 2.894 HGC (CM) + 1.795 FAC (CM)
A5 Isometric Endurance Limit (80%) = 42.4 + 0.410 Age (Y) - 4.3 Height (M) 0.568 BMI- 2.894 HGC (CM) + 1.795 FAC (CM)
112
Height effect: There are a very limited of studies that examined the effect of height on
isometric endurance limit. Chatterjee and Chowdhuri (1991) and Caldwell (1963) found
no relationship between height and isometric endurance limit. Figure 4-16 shows the
relationship between isometric endurance limit and height.
200
Isometric Endurance Limit (Sec)
180
160
140
120
100
Tall
80
Meduim
60
Short
40
20
0
80%
60%
40%
20%
MVC Percentage
Figure 4-16 Relationship between Isometric Endurance Limit and Height
The effect of height on isometric endurance limit is insignificant. However, subjects with
medium to tall height achieve higher endurance limits, especially in isometric endurance
limit (20%) condition. For other conditions, they almost have the same effect. The
general linear equations for isometric endurance limit with height effect are as follows:
Regression Equation (20%)
M Isometric Endurance Limit (20%) = 229 - 0.019 Age (Y) - 207.9 Height (M) 3.243 BMI + 3.38 HGC (CM) + 10.96 FAC (CM)
S
Isometric Endurance Limit (20%) = 207 - 0.019 Age (Y) - 207.9 Height (M) 3.243 BMI + 3.38 HGC (CM) + 10.96 FAC (CM)
T Isometric Endurance Limit (20%) = 224 - 0.019 Age (Y) - 207.9 Height (M) 3.243 BMI + 3.38 HGC (CM) + 10.96 FAC (CM)
113
Regression Equation (40%)
M Isometric Endurance Limit (40%) = 434.4 - 0.813 Age (Y) - 151.2 Height (M) 1.668 BMI - 7.07 HGC (CM) + 4.846 FAC (CM)
S Isometric Endurance Limit (40%) = 424.8 - 0.813 Age (Y) - 151.2 Height (M) 1.668 BMI - 7.07 HGC (CM) + 4.846 FAC (CM)
T Isometric Endurance Limit (40%) = 445.0 - 0.813 Age (Y) - 151.2 Height (M) 1.668 BMI- 7.07 HGC (CM) + 4.846 FAC (CM)
Regression Equation (60%)
M Isometric Endurance Limit (60%) = 279.8 - 0.236 Age (Y) - 112.2 Height (M) 1.097 BMI - 3.909 HGC (CM) + 2.839 FAC (CM)
S Isometric Endurance Limit (60%) = 270.0 - 0.236 Age (Y) - 112.2 Height (M) 1.097 BMI - 3.909 HGC (CM) + 2.839 FAC (CM)
T Isometric Endurance Limit (60%) = 290.1 - 0.236 Age (Y) - 112.2 Height (M) 1.097 BMI - 3.909 HGC (CM) + 2.839 FAC (CM)
Regression Equation (80%)
M Isometric Endurance Limit (80%) = 129.8 + 0.0754 Age (Y) - 44.0 Height (M) 0.493 BMI- 3.199 HGC (CM) + 1.775 FAC (CM)
S Isometric Endurance Limit (80%) = 124.7 + 0.0754 Age (Y) - 44.0 Height (M) 0.493 BMI - 3.199 HGC (CM) + 1.775 FAC (CM)
T Isometric Endurance Limit (80%) = 132.8 + 0.0754 Age (Y) - 44.0 Height (M) 0.493 BMI - 3.199 HGC (CM) + 1.775 FAC (CM)
BMI effect: BMI effect on isometric endurance limit has been studied by Crosby and
Wehbe (1994), Fraser et al. (1999), Montes (2001), Sheriff et al., (2012), Al Meanazel
(2013), and Minnal (2014). There is a positive correlation between physical factors and
isometric endurance limits. Figure 4-16 shows the relationship between isometric
endurance limit and BMI.
114
Isometric Endurance Limit (Sec)
600
500
400
S
300
M
200
L
100
0
20%
40%
60%
80%
MVC (Percentage)
Figure 4-17 Relationship between Isometric Endurance Limit and BMI
The effect of BMI on isometric endurance limit is insignificant. Isometric endurance
limits of subjects with small and medium BMIs are greater than those by subjects with
large BMIs by 8.83% (overall average). Large BMIs are associated with weakest readings
in three isometric endurance limit test conditions (40%, 60% and 80%) The condition of
20% results in more endurance than others. Results agreed with Funderburk et al. (1974)
who found a positive correlation between higher body physical factors (forearm
anthropometric BMI and hand muscle) with hand grip strength. Chatterjee and
Chowdhuri (1991) stated that holding time did not vary between persons with high and
low BMIs for isometric strength at 15% of the MVCs. The general linear equations for
isometric endurance limit with BMI effect are as follows:
Regression Equation (20%)
L Isometric Endurance Limit (20%) = -17.4 - 0.148 Age (Y) - 117.3 Height (M) 0.13 BMI+ 3.05 HGC (CM) + 10.78 FAC (CM)
M Isometric Endurance Limit (20%) = -6.2 - 0.148 Age (Y) - 117.3 Height (M) 0.13 BMI + 3.05 HGC (CM) + 10.78 FAC (CM)
115
S
Isometric Endurance Limit (20%) = 12.6 - 0.148 Age (Y) - 117.3 Height (M) 0.13 BMI+ 3.05 HGC (CM) + 10.78 FAC (CM)
Regression Equation (40%)
L Isometric Endurance Limit (40%) = 92.7 - 0.878 Age (Y) - 55.1 Height (M)
+ 3.81 BMI - 7.44 HGC (CM) + 4.914 FAC (CM)
M Isometric Endurance Limit (40%) = 125.0 - 0.878 Age (Y) - 55.1 Height (M)
+ 3.81 BMI - 7.44 HGC (CM) + 4.914 FAC (CM)
S Isometric Endurance Limit (40%) = 152.1 - 0.878 Age (Y) - 55.1 Height (M)
+ 3.81 BMI- 7.44 HGC (CM) + 4.914 FAC (CM)
Regression Equation (60%)
L Isometric Endurance Limit (60%) = 50.3 - 0.281 Age (Y) - 25.3 Height (M)
+ 0.997 BMI - 3.649 HGC (CM) + 2.849 FAC (CM)
M Isometric Endurance Limit (60%) = 67.2 - 0.281 Age (Y) - 25.3 Height (M)
+ 0.997 BMI - 3.649 HGC (CM) + 2.849 FAC (CM)
S Isometric Endurance Limit (60%) = 74.6 - 0.281 Age (Y) - 25.3 Height (M)
+ 0.997 BMI - 3.649 HGC (CM) + 2.849 FAC (CM)
Regression Equation (80%)
L Isometric Endurance Limit (80%) = 45.5 + 0.0472 Age (Y) - 9.7 Height (M)
+ 0.071 BMI- 2.928 HGC (CM) + 1.708 FAC (CM)
M Isometric Endurance Limit (80%) = 52.0 + 0.0472 Age (Y) - 9.7 Height (M)
+ 0.071 BMI- 2.928 HGC (CM) + 1.708 FAC (CM)
S Isometric Endurance Limit (80%) = 52.3 + 0.0472 Age (Y) - 9.7 Height (M)
+ 0.071 BMI- 2.928 HGC (CM) + 1.708 FAC (CM)
Hand grip circumference effect: Minnal (2014) and Al Meanazel (2013) found that
subjects with higher grip circumferences achieved more endurance limit. Figure 4-17
shows the relationship between isometric endurance limit and HGC.
116
200
Isometric Endurance Limit (Sec)
180
160
140
120
100
L
80
M
S
60
40
20
0
80%
60%
40%
20%
MVC Percentage
Figure 4-18 Relationship between Isometric Endurance Limit and HGC
The effect of HGC on isometric endurance limit is little. Greater HGC values are exerted
by subjects in the medium range. Large HGC values were observed in the following
conditions: 20%, 40%, 60% of the MVCs but not in the 80% condition, which might be
because of the nature of the experiment. Generally, larger HGCs can exert larger
isometric endurance limits. The general linear equations for isometric endurance limit
with HGC effect are as follows:
Regression Equation HGC (20%)
L Isometric Endurance Limit (20%) = 73 - 0.156 Age (Y) - 127.6 Height (M) 3.025 BMI + 3.96 HGC (CM) + 10.72 FAC (CM)
M Isometric Endurance Limit (20%) = 77 - 0.156 Age (Y) - 127.6 Height (M) 3.025 BMI + 3.96 HGC (CM) + 10.72 FAC (CM)
S Isometric Endurance Limit (20%) = 74 - 0.156 Age (Y) - 127.6 Height (M) 3.025 BMI + 3.96 HGC (CM) + 10.72 FAC (CM)
117
Regression Equation HGC (40%)
L Isometric Endurance Limit (40%) = 447.8 - 0.901 Age (Y) - 61.9 Height (M) 1.617 BMI - 13.79 HGC (CM) + 4.621 FAC (CM)
M Isometric Endurance Limit (40%) = 442.9 - 0.901 Age (Y) - 61.9 Height (M) 1.617 BMI - 13.79 HGC (CM) + 4.621 FAC (CM)
S Isometric Endurance Limit (40%) = 418.3 - 0.901 Age (Y) - 61.9 Height (M) 1.617 BMI - 13.79 HGC (CM) + 4.621 FAC (CM)
Regression Equation HGC (60%)
L Isometric Endurance Limit (60%) = 261.9 - 0.297 Age (Y) - 23.2 Height (M) 1.098 BMI - 9.31 HGC (CM) + 2.665 FAC (CM)
Isometric Endurance Limit (60%) = 255.4 - 0.297 Age (Y) - 23.2 Height (M) 1.098 BMI - 9.31 HGC (CM) + 2.665 FAC (CM)
S Isometric Endurance Limit (60%) = 238.1 - 0.297 Age (Y) - 23.2 Height (M) 1.098 BMI - 9.31 HGC (CM) + 2.665 FAC (CM)
Regression Equation (80%)
L Isometric Endurance Limit (80%) = 48.2 + 0.0499 Age (Y) - 11.7 Height (M) 0.499 BMI - 2.39 HGC (CM) + 1.901 FAC (CM)
M Isometric Endurance Limit (80%) = 53.2 + 0.0499 Age (Y) - 11.7 Height (M) 0.499 BMI - 2.39 HGC (CM) + 1.901 FAC (CM)
S Isometric Endurance Limit (80%) = 51.5 + 0.0499 Age (Y) - 11.7 Height (M) 0.499 BMI - 2.39 HGC (CM) + 1.901 FAC (CM)
Forearm circumference effect: There is also a limited number of studies investigating
the effect of FAC on isometric endurance limits. Anakwe et al. (2007) stated that
“Forearm circumference generally decreased with age for both men and women, although
this decline was less marked for women”. Figure 4-19 shows the relationship between
isometric endurance limit and FAC.
118
Isometric Endurance Limit(Sec)
180
160
140
120
100
APG
80
COMNAV
60
E&I
40
avg
20
0
ISOMETRIC
END, LIMIT
(20%)
ISOMETRIC
END, LIMIT
(40%)
ISOMETRIC
END, LIMIT
(60%)
ISOMETRIC
END, LIMIT
(80%)
avg
MVC Percentage
Figure 4-19 Relationship between Isometric Endurance Limit and FAC
Subjects with large FGCs exerted more isometric endurance limit for all fractions of the
MVCs percentages than those with medium and small FGCs. The general linear
equations for isometric endurance limit with FAC effect are as follows:
Regression Equation (20%)
L Isometric Endurance Limit (20%) = -156 - 0.243 Age (Y) - 128.0 Height (M) 2.850 BMI+ 3.71 HGC (CM) + 17.80 FAC (CM)
M Isometric Endurance Limit (20%) = -128.9 - 0.243 Age (Y) - 128.0 Height (M) 2.850 BMI+ 3.71 HGC (CM) + 17.80 FAC (CM)
S Isometric Endurance Limit (20%) = -99.8 - 0.243 Age (Y) - 128.0 Height (M) 2.850 BMI+ 3.71 HGC (CM) + 17.80 FAC (CM)
Regression Equation (40%)
L Isometric Endurance Limit (40%) = 283.6 - 0.813 Age (Y) - 66.5 Height (M) 1.774 BMI- 7.02 HGC (CM) + 4.99 FAC (CM)
M Isometric Endurance Limit (40%) = 282.2 - 0.813 Age (Y) - 66.5 Height (M) 1.774 BMI- 7.02 HGC (CM) + 4.99 FAC (CM)
119
S Isometric Endurance Limit (40%) = 283.2 - 0.813 Age (Y) - 66.5 Height (M) 1.774 BMI- 7.02 HGC (CM) + 4.99 FAC (CM)
Regression Equation (60%)
L Isometric Endurance Limit (60%) = 123.2 - 0.240 Age (Y) - 27.8 Height (M) 1.193 BMI- 3.857 HGC (CM) + 3.17 FAC (CM)
M Isometric Endurance Limit (60%) = 122.5 - 0.240 Age (Y) - 27.8 Height (M) 1.193 BMI- 3.857 HGC (CM) + 3.17 FAC (CM)
S Isometric Endurance Limit (60%) = 124.3 - 0.240 Age (Y) - 27.8 Height (M) 1.193 BMI- 3.857 HGC (CM) + 3.17 FAC (CM)
Regression Equation (80%)
L Isometric Endurance Limit (80%) = 98.7 + 0.0910 Age (Y) - 8.9 Height (M) 0.509 BMI
- 3.205 HGC (CM) + 0.876 FAC (CM)
M Isometric Endurance Limit (80%) = 92.8 + 0.0910 Age (Y) - 8.9 Height (M) 0.509 BMI
- 3.205 HGC (CM) + 0.876 FAC (CM)
S Isometric Endurance Limit (80%) = 91.3 + 0.0910 Age (Y) - 8.9 Height (M) 0.509 BMI
- 3.205 HGC (CM) + 0.876 FAC (CM)
Trade effect: A very limited number of studies take in consideration the effect of
different trades on isometric endurance limit. This dissertation examined the trade effect
on isometric endurance limit for aviation trades with five levels (APG: Airplane General,
E & I: Electrical and Instrument, COMNAV: Communication & Navigation, Eng:
Engine, and GSE: Ground Support Equipment). Figure 4-20 shows the relationship
between isometric endurance limit and trade.
120
ISOMETRIC ENDURANCE LIMIT (sec)
250
200
150
20%
40%
100
60%
50
80%
0
GSE
Engine
E&I
COMNAV
APG
MVC Percentage
Figure 4-20 Relationship between Isometric Endurance Limit and Trade
Subjects in APG and Eng trades achieved greater values in isometric endurance limit and
E& I whereas those in COMNAV achieve the lowest due to their nature of work. The
general linear equations for isometric endurance limit with trade effect are as follows:
Regression Equation (20%)
APG Isometric Endurance Limit (20%) = 144.7 - 0.267 Age (Y) - 93.5 Height (M) 1.753 BMI - 2.07 HGC (CM) + 9.76 FAC (CM)
COMNAV Isometric Endurance Limit (20%) = 128.0 - 0.267 Age (Y) - 93.5 Height (M)
- 1.753 BMI- 2.07 HGC (CM) + 9.76 FAC (CM)
E&I Isometric Endurance Limit (20%) = 153.5 - 0.267 Age (Y) - 93.5 Height (M) 1.753 BMI- 2.07 HGC (CM) + 9.76 FAC (CM)
ENG Isometric Endurance Limit (20%) = 179.0 - 0.267 Age (Y) - 93.5 Height (M) 1.753 BMI - 2.07 HGC (CM) + 9.76 FAC (CM)
GSE Isometric Endurance Limit (20%) = 109.8 - 0.267 Age (Y) - 93.5 Height (M) 1.753 BMI - 2.07 HGC (CM) + 9.76 FAC (CM)
121
Regression Equation (40%)
APG Isometric Endurance Limit (40%) = 253.8 - 0.928 Age (Y) - 65.3 Height (M) 1.567 BMI - 4.19 HGC (CM) + 4.123 FAC (CM)
COMNAV Isometric Endurance Limit (40%) = 224.3 - 0.928 Age (Y) - 65.3 Height (M)
- 1.567 BMI- 4.19 HGC (CM) + 4.123 FAC (CM)
E&I Isometric Endurance Limit (40%) = 225.2 - 0.928 Age (Y) - 65.3 Height (M) 1.567 BMI - 4.19 HGC (CM) + 4.123 FAC (CM)
ENG Isometric Endurance Limit (40%) = 236.4 - 0.928 Age (Y) - 65.3 Height (M) 1.567 BMI - 4.19 HGC (CM) + 4.123 FAC (CM)
GSE Isometric Endurance Limit (40%) = 238.9 - 0.928 Age (Y) - 65.3 Height (M) 1.567 BMI - 4.19 HGC (CM) + 4.123 FAC (CM)
Regression Equation (60%)
APG Isometric Endurance Limit (60%) = 111.3 - 0.293 Age (Y) - 28.7 Height (M) 1.167 BMI- 2.184 HGC (CM) + 2.612 FAC (CM)
COMNAV Isometric Endurance Limit (60%) = 96.6 - 0.293 Age (Y) - 28.7 Height (M) 1.167 BMI - 2.184 HGC (CM) + 2.612 FAC (CM)
E&I Isometric Endurance Limit (60%) = 97.0 - 0.293 Age (Y) - 28.7 Height (M) 1.167 BMI - 2.184 HGC (CM) + 2.612 FAC (CM)
ENG Isometric Endurance Limit (60%) = 101.2 - 0.293 Age (Y) - 28.7 Height (M) 1.167 BMI- 2.184 HGC (CM) + 2.612 FAC (CM)
GSE Isometric Endurance Limit (60%) = 103.6 - 0.293 Age (Y) - 28.7 Height (M) 1.167 BMI - 2.184 HGC (CM) + 2.612 FAC (CM)
Regression Equation (80%)
APG Isometric Endurance Limit 80%) = 54.6 + 0.0483 Age (Y) - 12.0 Height (M) 0.560 BMI - 1.880 HGC (CM) + 1.606 FAC (CM)
COMNAV Isometric Endurance Limit (80%) = 46.9 + 0.0483 Age (Y) - 12.0 Height (M)
- 0.560 BMI - 1.880 HGC (CM) + 1.606 FAC (CM)
E&I Isometric Endurance Limit (80%) = 47.7 + 0.0483 Age (Y) - 12.0 Height (M) 0.560 BMI- 1.880 HGC (CM) + 1.606 FAC (CM)
122
ENG Isometric Endurance Limit (80%) = 47.1 + 0.0483 Age (Y) - 12.0 Height (M) 0.560 BMI- 1.880 HGC (CM) + 1.606 FAC (CM)
GSE Isometric Endurance Limit (80%) = 54.7 + 0.0483 Age (Y) - 12.0 Height (M) 0.560 BMI - 1.880 HGC (CM) + 1.606 FAC (CM)
Race effect: There is a very limited number of studies investigating race effect. In this
dissertation, all experimental subjects were Jordanian. This could be considered as a
baseline for future studies that include different races, and as good comparisons for
middle-east studies. Table 4-27 shows anthropometric Data for Jordanian subjects. Table
4-28 shows descriptive statistics of experimental results on isometric endurance limit
from this experiment.
Table 4-27 Anthropometric Data for Jordanian Subjects
Variable
Mean
Standard
Minimum
Deviation
Age (Y)
41.712
7.833
25.000
Maximum
65.000
Weight(Kg )
82.60
12.85
55.00
114.00
Height (M)
1.7581
0.0705
1.5500
1.9300
BMI
26.679
3.600
18.711
37.422
HGC(CM)
22.523
1.338
19.500
25.500
FAC (CM)
29.341
2.441
23.000
35.00
123
Table 4-28 Descriptive Statistics: Isometric Endurance Limit
Variable
Mean
StDev
Minimum
Maximum
Isometric Endurance
Limit (20%)
167.45
61.94
60.00
343.00
Isometric Endurance
Limit (40%)
73.12
35.61
21.00
203.00
Isometric Endurance
Limit (60%)
38.37
21.90
9.00
116.00
Isometric Endurance
Limit (80%)
21.75
13.66
5.00
93.00
Smoking effect: Most researchers found that non-smokers can exert more force (Asano
and Branemark 1970; Isaac and Rand 1969; Davis 1960, and Al Meanazel 2013). Figure
4-21 shows the relationship between isometric endurance limit and smoking status.
ISOMETRIC ENDURANCE LIMIT (SEC)
200
180
160
140
120
100
Smoking
80
Non Smoking
60
40
20
0
80%
60%
40%
20%
MVC PERCENTAGE
Figure 4-21 Relationship between Isometric Endurance Limit and Smoking
On average, smokers exerted more isometric endurance limit than non-smokers by
12.98%. This might be because of (1) nature of the experiment where only medium to
124
low loads are studied, and (2) the mean age of the smokers. The general linear equations
for isometric endurance limit with smoking effect are as follows:
NS Isometric Endurance Limit (20%) = 60.8 - 0.083 Age (Y) - 114.9 Height (M) 2.617 BMI + 3.77 HGC (CM) + 9.85 FAC (CM)
S Isometric Endurance Limit (20%) = 75.1 - 0.083 Age (Y) - 114.9 Height (M) 2.617 BMI+ 3.77 HGC (CM) + 9.85 FAC (CM)
NS Isometric Endurance Limit (40%) = 271.7 - 0.791 Age (Y) - 60.3 Height (M) 1.539 BMI - 6.97 HGC (CM) + 4.567 FAC (CM)
S Isometric Endurance Limit (40%) = 280.0 - 0.791 Age (Y) - 60.3 Height (M) 1.539 BMI - 6.97 HGC (CM) + 4.567 FAC (CM)
NS Isometric Endurance Limit (60%) = 124.4 - 0.236 Age (Y) - 26.1 Height (M) 1.139 BMI - 3.813 HGC (CM) + 2.881 FAC (CM)
S Isometric Endurance Limit (60%) = 126.9 - 0.236 Age (Y) - 26.1 Height (M) 1.139 BMI- 3.813 HGC (CM) + 2.881 FAC (CM)
NS Isometric Endurance Limit (80%) = 67.2 + 0.0629 Age (Y) - 9.6 Height (M) 0.520 BMI - 3.137 HGC (CM) + 1.818 FAC (CM)
S Isometric Endurance Limit (80%) = 67.2 + 0.0629 Age (Y) - 9.6 Height (M) 0.520 BMI- 3.137 HGC (CM) + 1.818 FAC (CM)
Dominancy effect: Many researchers stated that isometric endurance limit for the
dominant hand is greater than the non-dominant hand. For example, Chatterjee and
Chowdhuri (1991) found that the dominant hand exerted the same load for a longer
period of time (15 seconds) than the non-dominant hand. Al Meanazel (2013) stated that
the dominant hand has the highest endurance limit. Figure 4-22 shows the relationship
between isometric endurance limit and dominancy. Note that there were 122 subjects
with a dominant right hand and 10 subjects with a dominant left hand.
125
200
ISOMETRIC ENDURANCE LIMIT (SEC)
180
160
140
120
100
Dominant
80
Non Dominant
60
40
20
0
80%
60%
40%
20%
MVC PERCENTAGE
Figure 4-22 Relationship between Isometric Endurance Limit and Dominancy
Results were very clear. Subjects have higher isometric endurance limit with the
dominant hand (3.56%) than the non-dominant one. The findings agreed with the
literature. The general linear equations for isometric endurance limit with dominancy
effect are as follows:
D Isometric Endurance Limit (20%) = 89.1 - 0.184 Age (Y) - 131.2 Height (M) 3.163 BMI + 3.45 HGC (CM) + 10.98 FAC (CM)
ND Isometric Endurance Limit (20%) = 106.4 - 0.184 Age (Y) - 131.2 Height (M) 3.163 BMI + 3.45 HGC (CM) + 10.98 FAC (CM)
D Isometric Endurance Limit (40%) = 278.7 - 0.809 Age (Y) - 65.3 Height (M) 1.765 BMI - 6.89 HGC (CM) + 4.960 FAC (CM)
ND Isometric Endurance Limit (40%) = 274.1 - 0.809 Age (Y) - 65.3 Height (M) 1.765 BMI- 6.89 HGC (CM) + 4.960 FAC (CM)
D Isometric Endurance Limit (60%) = 125.5 - 0.238 Age (Y) - 27.1 Height (M) 1.199 BMI - 3.757 HGC (CM) + 2.974 FAC (CM)
ND Isometric Endurance Limit (60%) = 122.5 - 0.238 Age (Y) - 27.1 Height (M) 1.199 BMI- 3.757 HGC (CM) + 2.974 FAC (CM)
126
D Isometric Endurance Limit (80%) = 64.1 + 0.0761 Age (Y) - 8.1 Height (M) 0.490 BMI- 3.047 HGC (CM) + 1.731 FAC (CM)
ND Isometric Endurance Limit (80%) = 59.3 + 0.0761 Age (Y) - 8.1 Height (M) 0.490 BMI
- 3.047 HGC (CM) + 1.731 FAC (CM)
4.7 Isotonic Endurance Limit Analysis and Discussion
The result of ANOVA performed on the isotonic endurance limit (20-60%) experimental
results are presented in this section. In addition, the predicted general linear and nonlinear
models for isotonic endurance limit were developed. ANOVA with 95% confidence level
was used to test the effects of independent factors. Also, different hypothesis-testing and
model adequacy checks were conducted. In particular, model assumptions of constant
variance, normality and independency were evaluated. Table 4-1 shows the dependent
factors, and independent variables with their levels. The ANOVA using design of
experiment with full general factorial regression analysis was performed with MINTAB
17. Table 4-29 shows outputs from ANOVA general factorial regression.
127
Table 4-29 ANOVA General Factorial Regression
Source
DF
Adj SS
Adj MS
F-Value
P-Value
Model
Linear
Posture
Age (Cat)
HD
Trade
Smoking
Height (Cat)
BMI (Cat)
HGC (Cat)
FAC (Cat)
2-Way Interactions
Posture*Age (Cat)
Posture*H D
Posture*Trade
Posture*Smoking
Posture*Height (Cat)
Posture*BMI (Cat)
Posture*HGC (Cat)
Posture*FAC (Cat)
Age (Cat) *Smoking
H D*Smoking
H D*BMI (Cat)
H D*HGC (Cat)
H D*FAC (Cat)
95
20
1
5
1
4
1
2
2
2
2
75
5
1
4
1
2
2
2
2
5
1
1
2
2
78633
16721
48
8073
740
4326
2281
926
755
3298
248
51746
763
103
1765
89
140
434
730
811
425
475
3938
503
827.71
836.06
48.18
1614.66
740.04
1081.47
2280.69
462.84
377.73
1649.00
123.80
689.94
152.59
103.05
441.37
89.00
70.07
217.12
365.21
0.89
162.29
425.36
237.56
1969.04
251.34
4.18
4.22
0.24
8.16
3.74
5.46
11.52
2.34
1.91
8.33
0.63
3.49
0.77
0.52
2.23
0.45
0.35
1.10
1.85
0.00
0.82
2.15
1.20
9.95
1.27
0.000
0.000
0.622
0.000
0.054
0.000
0.001
0.098
0.150
0.000
0.535
0.000
0.571
0.471
0.065
0.503
0.702
0.335
0.159
0.995
0.536
0.143
0.302
0.000
0.282
Trade*Smoking
Trade*Height (Cat)
Trade*BMI (Cat)
Smoking*Height (Cat)
Smoking*BMI (Cat)
4
8
8
2
2
3802
5814
6070
1166
82
950.43
726.75
758.75
583.14
41.14
4.80
3.67
3.83
2.95
0.21
0.001
0.000
0.000
0.054
0.812
Smoking*HGC (Cat)
Smoking*FAC (Cat)
Height (Cat)*BMI (Cat)
Height (Cat)*HGC (Cat)
Height (Cat)*FAC (Cat)
BMI (Cat)*HGC (Cat)
Error
Lack-of-Fit
Pure Error
Total
2
2
4
4
4
4
432
150
282
527
1537
4780
2517
1266
3955
5350
85501
49087
36414
164134
768.67
2390.18
629.35
316.42
988.68
1337.42
197.92
327.25
129.13
3.88
12.08
3.18
1.60
5.00
6.76
0.021
0.000
0.014
0.174
0.001
0.000
2.53
0.000
2
Figure 4-23 shows four residual plots: normal probability plot, the uniform distribution
against fits, uniform distribution against order, and normal histogram shape distribution.
128
Figure 4-23 Residual Plots for Isotonic Endurance Limit
Regression equations were derived for both general linear regression and general nonlinear regression models. Table 4-30 shows general linear models (MATLAB 15) and
general nonlinear models (MATLAB 15), respectively.
Table 4-30 Isotonic Endurance Limit Linear and Nonlinear Models
Model
Model Summary
RMSE: 16.9
Linear Models
Isotonic Endurance Limit = 29.495 + 0.15947AGE(Y) R-Sq: 0.0914,
55.559 HEIGHT (M) -0.57916 BMI + 3.558 HGC (CM) +
R-Sq,(Adj) 0.0827
1.1294 FAC (CM)
Nonlinear Models
Isotonic Endurance Limit = 33.635 + 0.0018137*
AGE(Y)^2 -16.255*HEIGHT(M)^2 -0.010432 *BMI^2 +
0.077773*HGC(CM)^2 + 0.0204*FAC (CM)^2
129
RMSE: 16.9
R-Sq: 0.0939,
R-Sq,(Adj) 0.0852
In Tables 4-31, 4-32 and 4-33 specific detailed grip strength models are shown for each
experimental condition, which enable us to compare with other researchers.
Table 4-31 Isotonic Endurance Limit General Linear Models (MATLAB 15)
Condition
Linear Regression Model
Errors
Isotonic
Endurance
20-60% LS,
RH
Isotonic
Endurance
20-60%
HS, RH
Isotonic
Endurance
20-60% HS,
LH
Isotonic
Endurance
20-60% LS,
LH
10.447 + 0.21693 AGE (Y) - 0.2437 WEIGHT(KG) - 15.791
HEIGHT (M) + 0.69384 BMI + 1.5815 HGC (CM) + 0.42891
FAC (CM)
RMSE: 15.7
R- SQ: 0.0493
407.28 + 0.35501 AGE (Y) + 2.1465 WEIGHT(KG) - 266.84
HEIGHT (M) - 7.4043 BMI + 1.3116 HGC (CM) + 2.4362 FAC
(CM)
RMSE: 14.1
R- SQ: 0.179
155.86, - 0.01367, AGE (Y) + 1.1824, WEIGHT(KG) - 168.78
HEIGHT (M) - 4.3126, BMI + 7.9354, HGC (CM) + 0.76883,
FAC (CM)
RMSE: 20.7
R- SQ: 0.183
65.081, + 0.1381 AGE (Y) + 0.1627 WEIGHT(KG) - 67.117
HEIGHT (M) - 1.1717 BMI + 3.3002 HGC (CM) + 0.72653
FAC (CM)
RMSE: 13.6
R- SQ: 0.102
Table 4-32 Isotonic Endurance Limit General Nonlinear Models (MATLAB)
Condition
Isotonic
Endurance 2060% LS, RH
Isotonic
Endurance 2060%
HS,
RH
Isotonic
Endurance 2060% HS,
LH
Isotonic
Endurance 2060% LS,
LH
Non Linear Equation
40.518 + 0.0025783 AGE (Y) ^2 - 0.00030012 WEIGHT(KG)^2
- 9.9457 HEIGHT(M)^2 + 0.0020933 BMI^2 + 0.034526
HGC(CM)^2 + 0.008021 FAC (CM)^2
133.28 + 0.0045011 AGE (Y) ^2 + 0.0065015 WEIGHT(KG)^2
- 47.88 HEIGHT(M)^2 - 0.076118 BMI^2 + 0.026819
HGC(CM)^2 + 0.042281 FAC (CM)^2
Errors
RMSE: 15.7
R- SQ: 0.0514
46.61 - 0.00062459 AGE (Y) ^2 + 0.003113 WEIGHT(KG)^2 31.131 HEIGHT(M)^2 - 0.041001 BMI^2 + 0.17612
HGC(CM)^2 + 0.013446 FAC (CM)^2
RMSE: 20.6
R- SQ: 0.185
37.953 + 0.0015277 AGE (Y) ^2 + 0.00015033
WEIGHT(KG)^2 - 15.986
HEIGHT(M)^2 - 0.014257 BMI^2
+ 0.071276 HGC(CM)^2 + 0.014364 FAC (CM)^2
RMSE: 13.6
R- SQ: 0.104
RMSE: 14
R- SQ: 0.191
Table 4-33 RMSE for Isotonic Endurance Limit in Linear and Nonlinear Regression
Condition
Isotonic Endurance
20-60%
LS, RH
RMSE Linear
Regression
(Matlab)
15.7
RMSE Non Linear
Regression(Matlab)
R- SQUARED
linear (Minitab)
15.7
0.0493
0.0514
HS, RH
14.1
14
0.179
0.191
HS, LH
20.7
20.6
0.183
0.185
13.6
16.025
13.6
15.975
0.102
0.12833
0.104
0.13285
lS, LH
Avg
130
R- SQUARED
nonlinear (Minitab)
The following paragraphs discuss effects of individual factors on isotonic endurance limit
including age, Trade, Smoking, BMI, Hand Grip Circumference, Dominancy, Forearm
Circumference, Posture, and Height.
Age effect: There is a limited number of studies about age effect on isotonic endurance
limit (20% -60% of MVC). Age has been classified into six intervals. According to the
literature review, subjects of all ages ranging from 10 to 99 years old have been studied.
Chatterjee and Chodhuri (1991), and Minnal (2014) and Al Meanazel (2013) considered
young ages between 18 and 25 years old. Koley et al. (2009) considered middle ages
(between 18 and 40 years old). Bohannon et al. (2006) considered old ages with ranges
from 75-79, 80-84, and 85-89 to 90-99 years old. In all cases, ages are classified based on
5-year ranges. Tables 4-34 and 4-35 showed the descriptive statistics and summary of
isotonic endurance, respectively.
Table 4-34 Descriptive Statistics of Isotonic Endurance Limit
Variable
Mean StDev
Minimum
Isotonic Endurance Limit,
38.32 15.72
6.00
20-60% LS, RH
Isotonic Endurance Limit,
33.73 15.18
9.00
20-60%, LS, RH
Isotonic Endurance Limit,
42.45 22.33
9.00
20-60%, HS, LH
Isotonic Endurance Limit,
30.67 13.99
7.00
20-60%, LS, LH
Maximum
110.00
80.00
109.00
85.00
Table 4-35 Summary of Isotonic Endurance Limit Test Regarding Age
Age group
LS, RH
HS, RH
HS, LH
LS, LH
Avg
A0: (25-<30)
31.67
27.22
29.89
36.64
31.355
A1: (30- <35)
37.55
39.45
48.73
28.24
38.493
A3: (40-<45)
38.88
28.3
43.09
27.63
A2: (35-<40)
36.63
32.85
37.3
33.16
34.985
A4: (45-<50)
38.47
35.56
46.58
36.22
A5: (Above 50)
Avg
48.22
38.57
47
35.06
40.78
41.06
30.11
32
39.208
41.528
131
34.475
36.674
ISOTONIC ENDURANCE LIMIT (SEC)
60
50
40
LS,D
30
LS,ND
HS,D
20
HS,ND
10
0
A0
A1
A3
A2
A4
A5
AGE PERIOD (SPEED & DOMINANCY)
Figure 4-24 Relationship between Age and Isotonic Endurance Limit for Different
Speed and Dominancy
The effect of age on isotonic endurance limit is observed for both conditions of (1) low
and high speed, and (2) left and right hand. Older subjects aged above 50 exerted the
most isotonic endurance limits whereas those with the youngest ages have the lowest
isotonic endurance limit. Findings for fraction grip strength agreed with Yassierli et al.
(2003), who stated that “interactive effects of age, gender, and effort level have
significant influence on fatigue and for grip strength relationship”. It disagreed with
Petrofsky and Linda (1975), who found no effect of age on isometric muscle strength.
The general linear equations for isotonic endurance limit with age effect are as follows:
A0 Isotonic Endurance Limit 20-60% l = 21.7 + 0.302 Age (Y)
0.669 BMI + 3.800 HGC (CM) + 1.358 FAC (CM)
A1 Isotonic Endurance Limit 20-60% l = 29.3 + 0.302 Age (Y)
0.669 BMI + 3.800 HGC (CM) + 1.358 FAC (CM)
A2 Isotonic Endurance Limit 20-60% l = 23.5 + 0.302 Age (Y)
0.669 BMI + 3.800 HGC (CM) + 1.358 FAC (CM)
A3 Isotonic Endurance Limit 20-60% l = 16.6 + 0.302 Age (Y)
0.669 BMI+ 3.800 HGC (CM) + 1.358 FAC (CM)
A4 Isotonic Endurance Limit 20-60% l = 20.7 + 0.302 Age (Y)
0.669 BMI + 3.800 HGC (CM) + 1.358 FAC (CM)
132
- 59.9 Height (M) - 59.9 Height (M) - 59.9 Height (M) - 59.9 Height (M) - 59.9 Height (M) -
A5 Isotonic Endurance Limit 20-60% l = 22.3 + 0.302 Age (Y) - 59.9 Height (M) 0.669 BMI + 3.800 HGC (CM) + 1.358 FAC (CM)
Height effect: There are a very limited number of studies that investigated the effect of
height on isotonic endurance limit. Figure 4-25 shows the relationship between height
and isotonic endurance limit.
50
45
ISOTONIC ENDURANCE LIMIT (SEC)
40
35
30
Tall
25
Meduim
20
Short
15
10
5
0
LS,D
LS,ND
HS,D
HS,ND
AVG
HIEGHT GROUP WITH (SPEED AND DOMINANCY)
Figure 4-25 Relationship between Height and Isotonic Endurance Limit
This dissertation examined the effect of height on isotonic endurance limit. Results show
that subjects with medium height exerted more isotonic endurance limit than other height
categories. The reason may be because they have the highest MVC and in good health.
The general linear equations for isotonic endurance limit with height effect are as
follows:
S: Isotonic Endurance Limit 20%-60% l = 25.7 + 0.1917 Age (Y) - 55.1 Height (M) 0.670 BMI+ 3.470 HGC (CM) + 1.272 FAC (CM)
133
M: Isotonic Endurance Limit End, 20%-60% l = 29.6 + 0.1917 Age (Y) - 55.1 Height
(M) - 0.670 BMI + 3.470 HGC (CM) + 1.272 FAC (CM)
T: Isotonic Endurance Limit 20%-60% l = 25.0 + 0.1917 Age (Y) - 55.1 Height (M) 0.670 BMI+ 3.470 HGC (CM) + 1.272 FAC (CM)
BMI effect: According to Sheriff et al. (2012), Montes (2001), Minnal (2014), Al
Meanazel (2013), and Fraser et al. (1999) and Crosby and Wehbe (1994), there is a
positive correlation between physical factors and isotonic endurance limit. Also, Stulen
and De Luca (1981) mentioned that MVC depends on muscles strength and brain-related
factors. Figure 4-26 shows the relationship between isotonic endurance limit and BMI.
50
Isotonic Endurance Limit (Sec)
45
40
35
30
25
Large
20
Meduim
15
Small
10
5
0
LS,D
LS,ND
HS,D
HS,ND
BMI Group (Speed & Dominancy)
Figure 4-26 Relationship between Isotonic Endurance Limit and BMI
Subjects with larger BMI exerted more isotonic endurance limit than other BMI
categories. Highest values were observed in (Isotonic Endurance Limit, 20-60% HS, LH)
condition. The general linear equations for isotonic endurance limit with BMI effect are
as follows:
L: Isotonic Endurance Limit 20%-60% l = 19.9 + 0.1636 Age (Y) - 53.9 Height (M) 0.256 BMI + 3.341 HGC (CM) + 1.191 FAC (CM)
134
M: Isotonic Endurance Limit 20%-60% l = 19.5 + 0.1636 Age (Y) - 53.9 Height (M) 0.256 BMI + 3.341 HGC (CM) + 1.191 FAC (CM)
S: Isotonic Endurance Limit 20%-60% l = 22.9 + 0.1636 Age (Y) - 53.9 Height (M) 0.256 BMI + 3.341 HGC (CM) + 1.191 FAC (CM)
Hand Grip Circumference (HGC) effect: Minnal (2014) and Al Meanazel (2013) found
that subjects with higher grip circumference exerted more MVC. Figure 4-27 shows
isotonic endurance limit versus HGC relationships.
60
ISOTONIC ENDURANCE LIMIT (SEC)
50
40
30
Large
Meduim
20
Small
10
0
HS,ND
HS,D
LS,ND
LS,D
HGC GROUP (SPEED & DOMINANCY)
Figure 4-27 Relationship between Isotonic Endurance Limit and HGC
Subjects with larger HGC exerted more isotonic endurance limit than other HGC
categories. The highest value is observed in (Isotonic endurance limit, 20-60% HS, LH)
condition. The general linear equations for isotonic endurance limit with HGC effect are
as follows:
135
L: Isotonic endurance limit 20-60% l = 23.9 + 0.1641 Age (Y) - 55.6 Height (M) 0.589 BMI + 3.79 HGC (CM) + 1.140 FAC (CM)
M: Isotonic endurance limit 20-60% l = 23.7 + 0.1641 Age (Y) - 55.6 Height (M) 0.589 BMI + 3.79 HGC (CM) + 1.140 FAC (CM)
S: Isotonic endurance limit 20-60% l = 24.9 + 0.1641 Age (Y) - 55.6 Height (M) 0.589 BMI + 3.79 HGC (CM) + 1.140 FAC (CM)
Forearm Circumference (FAC) effect: Anakwe et al. (2007) stated that forearm
circumference generally decreased with age for both men and women, although this
decline was less marked for women. Fraser et al. (1999) also mentioned “that British
subjects have slightly greater values for dominant forearm circumference measurements
in both men and women (29.1) cm Vs (24.3) cm for men and (25.6) cm vs (20.4) cm for
women”. Kallman et al. (1990) found that forearm circumference provides the best
practical measurement for MVC grip strength and muscle mass for both genders. Figure
4-28 shows the relationship between isotonic endurance limit and FAC.
ISOTONIC ENDURANCE LIMIT (SEC)
60
50
40
Large
30
Meduim
20
Small
10
0
HS,ND
HS,D
LS,ND
LS,D
FAC (SPEED & DOMINANCY)
Figure 4-28 Relationship between Isotonic Endurance Limit and FAC
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Table 4-36 Summary of FAC Effect in Isotonic Endurance Limit Test
Term
Isotonic Endurance
Limit, 20-60% LS, RH
Isotonic Endurance
Limit, 20-60%, LS, RH
Isotonic Endurance
Limit, 20-60%, HS, LH
Isotonic Endurance
Limit, 20-60%, LS, LH
Isotonic Endurance
Limit, Avg
Large
Medium
Small
Avg
40
38.03
37.53
38.52
41.5
31.27
32.38
35.05
50.57
40.33
40.15
43.68333
34.93
28.67
31.26
31.62
41.75
34.575
35.33
37.21833
Subjects with larger FACs exerted more isotonic endurance limits than subjects from
other FAC categories. The highest value was from (Isotonic Endurance Limit, 20-60%
HS, LH) condition. The general linear equations for isotonic endurance limit with age
effect are as follows:
L: Isotonic Endurance Limit, 20%-60% l = -1.2 + 0.1657 Age (Y) - 55.3 Height (M) 0.497 BMI
+ 3.437 HGC (CM) + 2.094 FAC (CM)
M: Isotonic Endurance Limit, 20%-60% l = -0.8 + 0.1657 Age (Y) - 55.3 Height (M) 0.497 BMI
+ 3.437 HGC (CM) + 2.094 FAC (CM)
S: Isotonic Endurance Limit, 20%-60% l = 6.5 + 0.1657 Age (Y) - 55.3 Height (M) 0.497 BMI
+ 3.437 HGC (CM) + 2.094 FAC (CM)
Trade effect: There was no literature considering the effect of different trades on isotonic
endurance limits. In this dissertation, the trade effect was examined on isotonic endurance
limit for five jobs (APG: Airplane General, E & I: Electrical and Instrument, COMNAV:
Communication and Navigation, Eng: Engine, and GSE: Ground Support Equipment).
Figure 4-28 shows the relationship between isotonic endurance limit and trades.
137
ISOTONIC ENDURANCE LIMIT (SEC)
60
50
40
LS,D
30
LS,ND
HS,D
20
HS,ND
10
0
APG
COMNAV
E&I
ENG
GSE
TRADE (SPEED AND DOMINANCY)
Figure 4-29 Relationship between Isotonic Endurance Limit and Trade for Different
Speeds and Dominancy
Highest isotonic endurance limits were exerted by subjects in engine trade, followed by
subjects in electrical and instrument than those in other trades whereas the lowest isotonic
endurance limits were observed for subjects in ground support equipment trade. The
highest isotonic endurance limit is exerted in (Isotonic Endurance Limit, 20-60% HS,
LH) condition. The general linear equations for isotonic endurance limit with trade effect
are as follows:
APG: Isotonic Endurance Limit, 20-60% = 39.7 + 0.1584 Age (Y) - 53.3 Height (M) 0.496 BMI + 2.906 HGC (CM) + 1.059 FAC (CM)
COMNAV: Isotonic Endurance Limit, 20-60% = 35.7 + 0.1584 Age (Y) - 53.3 Height
(M) - 0.496 BMI+ 2.906 HGC (CM) + 1.059 FAC (CM)
E&I: Isotonic Endurance Limit, 20-60% = 45.7 + 0.1584 Age (Y) - 53.3 Height (M) 0.496 BMI+ 2.906 HGC (CM) + 1.059 FAC (CM)
ENG: Isotonic Endurance Limit, 20-60% = 43.9 + 0.1584 Age (Y) - 53.3 Height (M) 0.496 BMI+ 2.906 HGC (CM) + 1.059 FAC (CM)
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GSE: Isotonic Endurance Limit, 20%-60% l = 32.9 + 0.1584 Age (Y) - 53.3 Height (M) 0.496 BMI
Race effect: There are a very limited number of studies investigating race effect. In this
dissertation, experimental subjects were all Jordanian. Table 4-37 showed the
anthropometric data of the subjects.
Table 4-37 Anthropometric Data
Variable
Mean
StDev
Minimum
Maximum
Age (Y)
41.712
7.833
25.000
65.000
Weight(Kg )
82.60
12.85
55.00
114.00
Height (M)
1.7581
0.0705
1.5500
1.9300
BMI
26.679
3.600
18.711
37.422
HGC(CM
22.523
1.338
19.500
25.500
FAC (CM)
29.341
2.441
23.000
35.00
Tables 4-38 and 4-39 showed the general linear and nonlinear models for isotonic
endurance limit.
Table 4-38 General Linear Models for Isotonic Endurance Limit
Isotonic Endurance
Limit, 20-60% LS,
Right
Isotonic Endurance
Limit, 20-60% HS,
RIGHT
Isotonic Endurance
Limit, 20-60% HS,
LEFT
Isotonic Endurance
Limit, 20-60% LS,
LEFT
Linear Regression Model
ISOTO, END 20-60% LS, RIGHT = 10.447 + 0.21693
AGE (Y) - 0.2437 WEIGHT(KG) - 15.791 HEIGHT (M) +
0.69384 BMI + 1.5815 HGC (CM) + 0.42891 FAC (CM)
ISOTO, END, 20-60% HS, RIGHT= 407.28 + 0.35501
AGE (Y) + 2.1465 WEIGHT(KG) - 266.84 HEIGHT (M) 7.4043 BMI + 1.3116 HGC (CM) + 2.4362 FAC (CM)
Errors
RMSE: 15.7
R- SQ: 0.0493
ISOTO, END, 20-60% HS, LEFT=155.86, - 0.01367,
AGE (Y) + 1.1824, WEIGHT(KG) - 168.78 HEIGHT
(M) - 4.3126, BMI + 7.9354, HGC (CM) + 0.76883,
FAC (CM)
Isotonic Endurance Limit, 20-60% LS, LEFT = 65.081, +
0.1381 AGE (Y) + 0.1627 WEIGHT(KG) - 67.117
HEIGHT (M) - 1.1717 BMI + 3.3002 HGC (CM) +
0.72653 FAC (CM)
RMSE: 20.7
R- SQ: 0.183
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RMSE: 14.1
R- SQ: 0.179
RMSE: 13.6
R- SQ: 0.102
Table 4-39 Nonlinear Regression Models for Isotonic Endurance Limit
Isotonic Endurance
Limit, 20-60% LS,
RIGHT
Isotonic Endurance
Limit, 20-60% HS,
RIGHT
Isotonic Endurance
Limit, 20-60% HS,
LEFT
Isotonic Endurance
Limit, 20-60% LS,
LEFT
Non Linear equation
Isotonic Endurance Limit, 20-60% LS, RIGHT = 40.518 +
0.0025783 AGE (Y) ^2 - 0.00030012 WEIGHT(KG)^2 9.9457 HEIGHT(M)^2 + 0.0020933 BMI^2 + 0.034526
HGC(CM)^2 + 0.008021 FAC (CM)^2
Isotonic Endurance Limit, 20-60% HS, RIGHT= 133.28 +
0.0045011 AGE (Y) ^2 + 0.0065015 WEIGHT(KG)^2 47.88 HEIGHT(M)^2 - 0.076118 BMI^2 + 0.026819
HGC(CM)^2 + 0.042281 FAC (CM)^2
Isotonic Endurance Limit, 20-60% HS, LEFT = 46.61 0.00062459 AGE (Y) ^2 + 0.003113 WEIGHT(KG)^2 31.131 HEIGHT(M)^2 - 0.041001 BMI^2 + 0.17612
HGC(CM)^2 + 0.013446 FAC (CM)^2
Isotonic Endurance Limit, 20-60% LS, LEFT= 37.953 +
0.0015277 AGE (Y) ^2 + 0.00015033 WEIGHT(KG)^2 15.986
HEIGHT(M)^2 - 0.014257 BMI^2 + 0.071276
HGC(CM)^2 + 0.014364 FAC (CM)^2
Errors
RMSE: 15.7
R- SQ: 0.0514
RMSE: 14
R- SQ: 0.191
RMSE: 20.6
R- SQ: 0.185
RMSE: 13.6
R- SQ: 0.104
This research was the first to examine the isotonic endurance limit for Jordanian subjects
for different speeds and hands including several independent factors.
Smoking effect: Most researchers such as Asano and Branemark (1970), Isaac and Rand
(1969), Davis (1960), and Al Meanazel (2013) found that non-smokers can exert more
force. Isaac and Rand (1969) states that smoking leads to profound vasoconstriction,
results in tissues starving from nutritive blood and bypassing from arterioles to venules.
Figure 4-30 shows the relationship between isotonic endurance limit and smoking.
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ISOTONIC ENDURANCE LIMIT 9SEC0
50
45
40
35
30
25
Smoking
20
Non Smoking
15
10
5
0
LS,D
LS,ND
HS,D
HS,ND
AVG
EXPERIMENTAL CONDITIONS (SPEED & HAND DOMINANCY)
Figure 4-30 Relationship between Isotonic Endurance Limit and Smoking
The effect of smoking on isotonic endurance limit considering (1) low and high speed
and (2) left and right hand is examined. Results show that smokers exerted more isotonic
endurance limit than nonsmokers by a small percentage (1.85%). Due to the experimental
nature, this dissertation concludes that no effect of smoking on highest isotonic
endurance limit, which is again exerted in (Isotonic Endurance Limit, 20-60% HS)
condition. The general linear equations for isotonic endurance limit with smoking effect
are as follows:
NS: Isotonic Endurance Limit, 20-60% l = 29.7 + 0.1589 Age (Y) - 55.7 Height (M) 0.584 BMI + 3.558 HGC (CM) + 1.138 FAC (CM)
S: Isotonic Endurance Limit 20-60% l = 29.5 + 0.1589 Age (Y) - 55.7 Height (M) 0.584 BMI
+ 3.558 HGC (CM) + 1.138 FAC (CM)
Hand dominancy effect: Many research studies such as Chatterjee and Chowdhuri
(1991) stated that “isotonic endurance limit for dominant hand is greater 15 Seconds
extra than the non-dominant hand and 16 Seconds more than the non-dominant hand”. Al
141
Meanazel (2013) stated that the dominant hand has the highest endurance limit, and hand
dominant strength is affected by age since the dominant hand is used more frequently as a
person ages. The following analysis checks the effect of variable independent factors on
dominancy issue. Figure 4-31 shows the relationship between isotonic endurance limit
and dominancy. There was 122 subjects with the right hand as dominant and 10 subjects
39
38
37
36
35
34
33
32
31
Isoto,End,20%-60% lS
Dominancy Effect)
NonDominant
Dominant
Isotonic Endurance Limit (Sec)
with left hand as dominant.
Figure 4-31 Relationship between Isotonic Endurance Limit and Hand Dominancy
The effect of hand dominancy on isotonic endurance limit considering (1) low and high
speed, and (2) left and right hand is examined. Results show that there is almost no effect
of dominancy on isotonic endurance limit. Highest values of isotonic endurance limit are
exerted in (Isoto, End, 20-60% HS, LH) condition. Note that highest values of isotonic
endurance limit are exerted by subjects aged above 50 years old and isotonic endurance
limit decreases as the age of the subject decreases. This finding confirmed some studies
in the literature. For example, Chatterjee and Chowdhuri (1991) found that the dominant
142
hand sustained extra isotonic endurance limit (on average 16 seconds) more than the nondominant hand. Sorensen et al. (2009) found “endurance of dominant hand (is) 15
seconds more than the non-dominant hand”. Al Meanazel (2013) observed that the
dominant hand has the highest endurance limit. The general linear equations for isotonic
endurance limit with hand dominancy effect are as follows:
Dominant and Non-dominant Hand
D: Isotonic Endurance Limit, 20%-60% l = 30.0 + 0.1571 Age (Y) - 55.8 Height (M) 0.584 BMI + 3.542 HGC (CM) + 1.145 FAC (CM)
ND: Isotonic Endurance Limit, 20%-60% l = 30.9 + 0.1571 Age (Y) - 55.8 Height (M) 0.584 BMI + 3.542 HGC (CM) + 1.145 FAC (CM)
Right and Left Hand
L: Isotonic Endurance Limit, 20%-60% l = 29.8 + 0.1595 Age (Y) - 55.6 Height (M) 0.579 BMI+ 3.558 HGC (CM) + 1.129 FAC (CM)
R: Isotonic Endurance Limit, 20%-60% l = 29.2 + 0.1595 Age (Y) - 55.6 Height (M) 0.579 BMI + 3.558 HGC (CM) + 1.129 FAC (CM)
4.8 MODELING WITH NEURAL NETWORK
Both neural network coding and toolbox in Matlab 15 calculate the maximum voluntary
contraction (MVC) for the following outputs:
1. MVC
2. MVC (Kg, Sit, D)
3. MVC (Kg, Sit, ND)
4. MVC (Kg, Stand, D)
5. MVC (Kg, Stand, ND)
6. Isometric Endurance Limit (20%)
7. Isometric Endurance Limit (40%)
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8. Isometric Endurance Limit (60%)
9. Isometric Endurance Limit (80%)
10. Isometric Endurance Limit (Avg)
11. Isotonic Endurance Limit 20-60% Low, S, Right
12. Isotonic Endurance Limit 20-60% High, SP, Right
13. Isotonic Endurance Limit 20-60% High, SP, Left
14. Isotonic Endurance Limit 20-60% Low, SP, Left
15. Isotonic Endurance Limit 20-60% (Avg)
The following continuous inputs were used:
1. X1: Age (Y)
2. X2: Height (M)
3. X3: BMI
4. X4: HGC (CM)
5. X5: FAC (CM)
The experiment assumptions are as follows:
1- Training set: 70% or 358 samples where the neural network was adjusted and
attuned according to its error.
2- Validation set: 15% or 79 samples; to find and measure neural network
generalization, and to stop the training process when generalization achieves the
highest accuracy and the process stops improving.
3- Testing set: 15% or 79 samples, as an independent measure of neural network
performance.
4- Number of hidden neurons: 10 neurons.
5- General learning algorithm used is backpropagation since it is an effective
algorithm to adjust the weight on each node created by data. Input training set was
chosen similar to Heaton (2005). It is generally used when there is a large amount
144
of input/output and the relationship between those inputs and outputs is complex or
unknown.
6- Training algorithem used is Levenberg–Marquardt where it takes less time using
more memory and stops when generlization achieves the most performance as
indicated by increase in mean square error. Backpropagation could be used as well.
Similar to Beale et al. (1998), Levenberg-Marquardt algorithm was selcted due to
its fast adjustment mechanisms.
7- The experiment information used in a feed-forward neural network is transferred in
only one direction; that is, it moves from the input layer through the hidden layer
and then to the output layer.
8- Hidden layer may contain one or more hidden layers .
9- Validation checks: 6
Figure 4-31 shows a general diagram of the three layers of nodes in a neural network.
Mean square errors and R value are shown in Tables 4-40, and Table 4-41, 4-42, 4-43 for
the three tests: MVC, isometric and isotonic endurance limits. Theses values are small
implying that neural network achieved good performance.
Table 4-40 Summary of Neural Network Performance (MVC, Isometric and
Isotonic Endurance Limits)
MVC
Isometric Endurance
Isotonic Endurance Limit
Limit
MSE
R
MSE
R
MSE
R
7.09 e -8
9.9 e-1
3.35 e-7
9.9 e-1
1.2 e-3
9.9 e-1
1.56 e-7
9.9 e-1
3.4 e-7
9.9 e-1
6.5 e-4
9.9 e-1
7.51 e-8
9.9 e-1
2.54 e-7
9.9 e-1
2.4 e-3
9.9 e-1
145
Figure 4-32 General Neural Network Diagram
Table 4-41 Neural Network Performance for MVC Test
MVC
MSE
R-Sq
7.09 e -8
9.9 e-1
1.56 e-7
9.9 e-1
7.51 e-8
9.9 e-1
Table 4-42 Neural Network Performance for Isometric Endurance Limit
Isometric Endurance Limit
MSE
R-Sq
3.35 e-7
9.9 e-1
3.4 e-7
9.9 e-1
2.54 e-7
9.9 e-1
146
Table 4-43 Neural Network Performance for Isotonic Endurance Limit
Isotonic Endurance Limit
MSE
R-Sq
1.2 e-3
9.9 e-1
6.5 e-4
9.9 e-1
2.4 e-3
9.9 e-1
Neural network performance on isotonic endurance limit for the training set is shown in
Table 4-46, where experiment samples are divided into three parts (training, validation
and testing). First, the training data set is used to build the neural network. Neural
network training continues given that the neural network continues improving while
checking with the validation set. The neural network training stopping point is
highlighted in green. Neural network performance for the three tests is shown in Table 446. It shows the neural network performance improvement during the training process. In
neural networks, the performance is calculated in terms of mean squared error (MSE; Y
axis log scale). MSE rapidly decreased as the network was developed and trained. Table
4-4 shows that the best validation performance was at 1.5 e-7 at epoch 554 for MVC and
3.41 e-7 at epoch 1000 for isometric endurance limit and .0000655 at epoch 16 for the
test of isotonic endurance limit. In this research, all results are reasonable since the final
MSEs are very small. The testing and validations errors are similar, and no significant
over fitting has occurred.
147
Table 4-44 Neural Network Performance for the Three Tests
MVC
Isometric
Endurance
Limit
Isotonic
Endurance
Limit
148
Other neural network performance measure includes the error histogram. Table 4-45
shows the error size distribution. Most errors are near zero, as viewed for the three tests
(MVC, isometric and isotonic endurance limits).
Table 4-45 Neural Network Error Histogram
MVC
Isometric
End Limit
149
Isotonic
Endurance
Limit
Neural network function fit plot is shown in Table 4-46. Besides, it plots the experiment
targets. The error bars show the difference between inputs and outputs which is very little
for all neural network model of MVC, isometric and isotonic endurance limits.
150
Table 4-46 Neural Network Function Fit Plot
MVC
Isometric
Endurance
Limit
151
Isotonic
Endurance
Limit
4.9 Neural Network Regression
Other ways of measuring performance of neural network (i.e., how neural network fits
the data) include the regression plots. In the dissertation, the regression plots are
generated for the three tests. It plots the neural network outputs against experiment target
values. Table 4-47 shows that the neural network models have learned and fitted the
experiment data well. Outputs match the experiment targets accurately for the three
datasets (training , testing , and validation) sets. R values equal 1.
152
Table 4-47 Neural Network Regression Plots for the Three Tests
MVC
Isometric
Endurance
Limit
153
Isotonic
Endurance
Limit
4.9 ANFIS Analysis
ANFIS analysis is performed in this section. Table 4-48 and 4-49 shows those overall
ANFIS output errors and those for each experimental condition, respectively. By
examining the output over the whole training period, it is clear that the experimental
checking dataset obtains minimum checking error. Also, step-size errors show very small
numbers which serves to adjust references for the initial step-size, and increasing and
decreasing rates. In general, the checking error should decrease until the training assigned
point, and then increases. This point is called model over fitting point. Detailed ANFIS
info is as follows:
o Number of nodes: 1016
o Number of linear parameters: 2916
o Number of nonlinear parameters: 54
o Total number of parameters: 2970
o Number of training data pairs: 100
154
o Number of checking data pairs: 0
o Number of fuzzy rules: 486
o Epoch completed at: 49, 50
Table 4-48 ANFIS Output Errors for the Three Tests (MVC, Isometric and Isotonic
Endurance Limits)
Test Results
Error
MVC
3.73432 Step size (0.005905)
Isometric Endurance Limits
4.2323e-05 Step size (0.008100)
Isotonic Endurance Limits
3.6203e-05 (0.006561)
Table 4-49 ANFIS Output Errors for Each Experimental Condition
Test Results
Error
MVC (Kg, Sit, D)
3.84522e-05
MVC (Kg, Sit, ND)
2.46537e-05
MVC (Kg, Stand, D)
3.6203e-05
MVC (Kg, Stand, ND)
1.56111e-05
Isometric Endurance Limit (20%)
0.000128428
Isometric Endurance Limit (40%)
5.33146e-05
Isometric Endurance Limit (60%)
2.26027e-05
Isometric Endurance Limit (80%)
3.80123e-05
Isotonic Endurance Limit, 20-60% low, 3.00345e-05
SP, RH
Isotonic Endurance Limit, 20-60% High, 1.73763e-05
SP, RH
Isotonic Endurance Limit, 20-60% High, 4.6505e-05
SP, LH
Isotonic Endurance Limit, 20-60% low, 4.61178e-05
SP, LH
155
Figure 4-33 ANFIS Diagram
156
CHAPTER FIVE CONCLUSIONS AND FUTURE WORK
5.1 CONCLUSION ON MATHEMATICAL MODELING
Experimental studies were conducted with a psychophysical approach to examine the
effect of static/dynamic forces, on the hand grip fatigue and strength, maximum voluntary
contraction (MVC), fatigue limits, and endurance for subjects in the aviation industry. In
this comprehensive research, nine independent factors were considered which are most
likely to represent all possible factors considered by other researchers during the last 60
years. To fill a significant literature gap, several new factors had been investigated for
their effects on MVC and hand muscle fatigue, including new apparatus (digital
dynamometer), hand volume, forearm grip circumference, new race (Jordanian subjects),
new posture (standing and sitting), large smoker sample, and middle-age to older (from
25 to 55 years old) subjects. The uniqueness and significance of the research was
illustrated in the application to engineers and different trade’s mechanics in the aviation
industry, where a combination of isometric and dynamic isotonic forces is applied in
performing tasks. Whereas the results from this dissertation verify other researchers'
work, it also proposes comprehensive models considering nine different factors. Finally,
this research could be considered as a standard procedure for comparisons and
conducting future research. Results were analyzed by many statistical test, mathematical
modeling and machine learning techniques. General, detailed, and precise models
(mathematical and Artificial Neural Network and ANFIS models) were developed to
predict MVC, maximum isometric endurance limit of submaximal (20%, 40%, 60% and
157
80%) of MVC, and isotonic fatigue endurance between 20% and 60% of MVC. The
experimental results were presented in three sections, and each section were analyzed in
the following manner: Part (1) focuses on maximum voluntary contraction (MVC), Part
(2) is devoted to isometric muscle fatigue limit for different MVC ratios (20%, 40%, 60%
and 80%), and Part (3) studies isotonic muscle fatigue for between 20% and 60% of the
MVC force. Each part considers outputs of four special cases and nine independent
factors (between 2-6 levels) as shown in Table 5-1 with a total of 29 levels. In contrast to
many studies in the literature, this dissertation considers all factors which might have a
significant effect. Literature review showed that most of other researchers reported their
findings with simple comparisons. This dissertation also reported descriptive statistics for
comparisons with other researchers' findings. Both linear and nonlinear modeling for
each independent factor was performed. All independent factors had correlation effects as
expected, since most of them are related to subjects' physical factors (of the human body)
such as forearm, hand grip circumference, height, weight, and body mass index. The
correlation effect appeared only as negative between MVC and isotonic endurance limit
(low & high speed) and between age and height for experimental subjects, and positive
between MVC values and isometric endurance limit at 20% of the MVC, and between
isometric endurance limit and isotonic endurance limit. Experiment data for MVC and
Isometric endurance limit followed normal distributions. Box-cox transformation was
used for isotonic endurance limits. Also, all potential outliers had been investigated for
validity. Subject group ages from 25 to 60 years old, with the following basic statistic:
age (41.71 years old), weight (82.6 Kg), height (1.75 m), BMI (26.67), hand grip
circumference (22.52 cm), and forearm circumference (29.34 cm). Detailed data are
158
provided in the Appendices for all ages and trades. Subjects from the electrical and
instrument trade are the youngest and heaviest among all trades, with height, HGC and
FAC being almost the same as all other trades. The summary of MANOVA results
included mostly all independent factors: (1) age, height, trade, forearm circumference
(FAC), hand grip circumference (HGC) and BMI for MVC; (2) trade, forearm
circumference (FAC), hand grip circumference (HGC) for isometric endurance limit; and
(3) age, height, trade, forearm circumference (FAC) and hand grip circumference (HGC)
for isotonic endurance limit. In this study, ANOVA was conducted with full factorial
experimental design on the following factors: age (6 levels: A0, A1, A2, A3, A4, A5),
trade (5 levels: COMNAV, ENG, GSE, APG, E&I), height (3 levels: short, medium, tall),
BMI (3 levels: large, medium, and small), hand grip circumference (HGC; 3 levels: large,
medium, and small), forearm grip circumference (FAC; 3 levels: large, medium, and
small), dominancy (2 levels: dominant and non-dominant), and posture (2 levels: sitting
and standing). MANOVA/ANOVA tests verify all independent factors as significant
factors. Residual plots show that the model fit in ANOVA and regression analysis is
satisfactory. The normal probability plot of residuals shows that the normality assumption
holds, since it is forming a straight line with few points that depart from the straight line.
The plot of residuals versus fitted values tests the constant variance assumption and
shows the pattern (random) of the experiment residuals on both sides of the graph, with
no data points far away from the majority of points, i.e., outliers. The histogram of the
residuals shows the general characteristics of experimental data and plots the residuals
that include typical values, spread and shape. The plot shows no skewed distribution. The
plot of residuals versus order of data shows a correlation between experimental factors
159
and collected data. The plots of both main effects and interactions confirm results from
the ANOVA regarding significant factors. Table 5-1 shows the General Linear and
Nonlinear Models for MVC Test.
Table 5-1 General Linear and Nonlinear Models for MVC Test (MATLAB 15)
MVC= -21.594 -0.43487 AGE(Y) + 22.073 HEIGHT RMSE: 6.31
Linear
(M) -0.36207 + BMI 0.14221 HGC (CM) +
R-Sq: 0.448,
Model
1.8439 FAC (CM)
R-Sq,(Adj) 0.443
Non
Linear
Model
MVC= 13.786 + -0.0051191 * AGE(Y)^2 +
6.0779*HEIGHT(M)^2 -0.006859 *BMI^2 +
0.0028544*HGC(CM)^2 + 0.030977*FAC (CM)^2
RMSE: 6.3
R-Sq: 0.451,
R-Sq,(Adj) 0.445
Detailed general linear and nonlinear models and stepwise models for the three tests
(Maximum Voluntary Contraction, Isometric Endurance Limit and Isotonic Endurance
Limit) are obtained for the following independent factors: Age (6 levels), Trade (5
levels), Height (3 levels), BMI (3 levels), Hand grip circumference (HGC; 3 levels),
Forearm grip circumference (FAC (3 levels), Dominancy (2 levels), and Posture (2
levels). These detailed models establish a baseline for future studies and will be easier for
comparisons. Tables 5-2 to 5-11 shows the detailed independent factors effect with
conclusion for MVC
Table 5-2 Posture Effect on MVC
Factor
posture
(standing,
sitting)
Findings
Conclusion
1. Aviation industry subjects exerted
 Standing Posture Avg: 46.6 Kg
almost same MVC in both postures
 Sitting Posture Avg : 46.255 KG
2.
Highest MVC value was in (30 Standing/Sitting (overall) Percentage
<35) age group followed by A0:
extra with .07%
(25-<30) age group
3. Lowest MVC value in older ages
(above 50 year old)
160
Table 5-3 Age Effect on MVC
Factor Findings
Age
MVC (KG) (SIT, D)
Conclusion
1- Aviation industry subjects exerted
different MVC for different age
Highest MVC: A3 (40-<45) : 49.73
groups
Lowest MVC: A5 (above 50): 39.18
Same MVC: A3 (40-<45), A1 (30- 2- Highest MVC value was in (30<35) age group followed by A0:
<35), A2 (35-<40)
(25-<30) age group
3- Lowest MVC value in older ages
MVC (KG) (SIT, ND)
(above 50 years old)
Highest MVC: A0 (25-<30): 50.76
Lowest MVC: A5 (above 50): 36.18
Same MVC: A0 (25-30), A1(30- <35)
MVC (KG) (STAND, D)
Highest MVC: A3(40-<45): 51.43
Lowest MVC: A5 (above 50): 40.73
Same MVC: A3 (40-<45), A1 (30<35)
MVC (KG) (STAND, ND)
Highest MVC: A1(30- <35): 49.04
Lowest MVC: A5(above 50): 36.99
Same MVC: A1(30-<35),A0(25-<30)
Table 5-4 Height effect on MVC
Factor
Height
Findings
Conclusion
MVC (Kg,Sit,D) T (53.58), M(7.080 ),
S(42.21),
1. Height has a major effect
on MVC
2. Taller people exerted
more MVC than medium
(9.1%)
and
shorter
(12.21%)
MVC (Kg,Sit,ND) T (49.59), M (46.633), S
(41.41)
MVC (Kg,Stand,D) T (54.59), M48.430 ), S
(44.23)
MVC (Kg,Stand,ND) T (47.75), M (46.090), S
(40.36)
161
Table 5-5 BMI Effect on MVC
Factor
Findings
Conclusion
BMI
MVC (Kg, Sit, D) L(47.75), M( 47.78),
S(46.35)
MVC (Kg, Sit, ND) L( 45.88), M (46.831),
S (44.90)
MVC (Kg, Stand, D) L(48.22), M (49.72),
S (47.74 )
MVC (Kg, Stand, ND) L(46.03), M(45.94),
S (43.24 9.07)
1. BMI has a minor effect on
MVC
2. Medium BMI subjects exerted
higher MVC than large BMI
subjects (by 1.2%) and small
BMI subjects (by 4.43%)
3. Highest MVC exerted in
MVC
(Kg,
Stand,
D)
condition.
Table 5-6 Hand Grip Circumference (HGC) Effect on MVC
Factor
Height
Findings
MVC (Kg,Sit,D) LARGE
(52.05),MEDIUM (46.56),SMALL
(44.23)
MVC (Kg,Sit,ND) LARGE (50.04),
MEDIUM (45.61), SMALL (42.84)
MVC (Kg,Stand,D) LARGE
(53.26), MEDIUM (48.27), SMALL
(45.38),
MVC (Kg,Stand,ND) LARGE
(49.77), MEDIUM (44.47), SMALL
(41.62)
Conclusion
1. HGC has a major effect on MVC
2. Subjects exerted more MVC when
they have larger FGC
3. Highest MVC exerted in MVC (Kg,
Stand, D) condition
Table 5-7 Forearm Circumference (HGC) Effect on MVC
Factor
Findings
Conclusion
Height
MVC (Kg, Sit,D, LARGE(53.48),
MEDIUM(46.41), SMALL(43.85)
MVC (Kg,Sit,ND, LARGE(51.53),
MEDIUM(45.65), SMALL(41.91)
MVC (Kg,Stand,D), LARGE(55.51),
MEDIUM(48.07), SMALL(44.44),
MVC(Kg,Stand,ND) LARGE(52.89),
MEDIUM(46.215), SMALL(40.6)
1. FAC has a major effect on
MVC
2. Subjects exerted more MVC
when they have larger FGC
3. Highest MVC exerted in
MVC (Kg, Stand, D)
condition
162
Table 5-8 Trade Effect on MVC
Factor
Findings
Conclusion
Height
MVC (Kg,Sit,D), APG (47.03), COMNAV 1. Trade has a minor effect
on MVC (All trades
(47.27), E&I (48.02), ENG (47.45) ,GSE(47.14)
MVC (Kg,Sit,ND), APG (46.06 ), COMNAV
(45.10), E&I (44.58), ENG (46.00), GSE (46.8)
MVC (Kg,Stand,D), APG (48.31), COMNAV
(48), E&I (49.77), ENG (49.15), GSE (49.15)
MVC (Kg,Stand,ND) APG (44.94), COMNAV
(42.99), E&I (46), ENG (45.88), GSE (44.33)
mostly exerted the same
MVC)
2. Highest MVC exerted
by engineers and E& I
trades
3. Consider mean age for
trades and smoking
status.
4. Engineer and E& I have
the mean ages 42 and
37, respectively
Table 5-9 Race Effect on MVC
Population
MVC (Kg) MVC (Kg) Author(s) (Year)
(Male)
(Female)
Singaporean
Indian
24.1
30-39.8
N/A
22.75
Jordan (Pilot Study)
33.619
N/A
Incel et al. (2002)
Vaz et al. (1998, 2002), Koley et al.
(2009)
Al-Momani (2015)
Spanish
39.95
25.72
Heredia et al. (2005)
Scotland
35.12
23.02
Heredia et al. (2005)
Scotland
40.0–48.8
27.5–34.4
Brenner et al. (1989)
Jordan
46.58167
N/A
Al-momani (2015)
USA
62.0
37.0
Crosby & Wehbe (1994)
USA
44.8
35.0
Al Meanazel (2013)
Heredia et al. (2005) found that Jordanian subjects exerted higher MVC than
Singaporean, Indian, Spanish and Scotland subjects, and less than UK and USA subjects;
however, this result cannot be considered conclusive since each experiment has its
environment and different subjects. The research considers race factor as an important
163
factor since it is related to culture, lifestyle, and physical factors of human races in
general. Table 5-10 Smoking Effect on MVC
Factor
Findings
Conclusion
Smokers
MVC (Kg,Sit,D), S(47.59)
MVC (Kg,Sit,ND), S(46.52)
MVC (Kg,Stand,D), S(49.52)
MVC (Kg,Stand,ND), S(45.39)
1. Smokers exerted more MVC than
non-smokers by 2%
2. Difference is not high
3. Highest MVC was exerted in
MVC (Kg, Stand, D) condition
Non
smokers
MVC (Kg,Sit,D),
MVC (Kg,Sit,ND),
MVC (Kg,Stand,D),
MVC (Kg,Stand,ND),
NS(46.822)
NS(45.53)
NS(47.99)
NS(44.44)
Table 5-11 Dominancy Effect on MVC
Factor
Findings
Conclusion
Dominancy
(standing, sitting)
Standing
1. Dominant hand exerted more MVC
Dominant=48.26kg
by 7.41%,
Standing
non 2. Non-dominant hand exerted more
Dominant:44.93 kg
MVC by 1.41%
Sitting Dominant: 46.58kg
3. The highest MVC was exerted by the
Sitting non dominant:45.93
dominant hand of subjected aged 30kg
45 years old, followed by those 25-30
years old; the MVC decreased for
subjects above 45 years old
5. Non-dominant hand of the younger
subjects aged 25-30 years old exerted
more MVC, followed by 30-35 years
old; the MVC decreased above age 35
years old .
164
Tables 5-12 through 5-32 shows the independent factors effect and detailed conclusions
for Isometric fatigue limits)
Table 5-12 General Linear Models for Isometric Endurance Limit
Isometric Endurance Limit
(20%)
Isometric Endurance Limit
(40%)
Isometric Endurance Limit
(60%)
Isometric Endurance Limit
(80%)
Isometric Endurance Limit
(Avg)
Linear Regression Model
Errors
127.25 + -0.0014601 * AGE(Y)^2 + 37.427
*HEIGHT(M)^2 + 0.05763*BMI^2 +
0.084164*HGC(CM)^2 + 0.18183*FAC
(CM)^2
RMSE: 58.4
R-Sq: 0.116
R-Sq,(Adj)
0.107
177.76 -0.0072556* AGE(Y)^2 18.581*HEIGHT(M)^2 0.035377*BMI^2 0.16351*HGC(CM)^2 + 0.086309*FAC
(CM)^2
RMSE: 33.5
R-Sq: 0.117
R-Sq,(Adj)
0.109
83.723-0.0016898 b2* AGE(Y)^2 8.1407*HEIGHT(M)^2 0.024757*BMI^2 0.088919*HGC(CM)^2 + 0.05318*FAC
(CM)^2
RMSE: 21
R-Sq: 0.0835,
R-Sq,(Adj)
0.0747
44.498 + 0.0016698* AGE(Y)^2 2.8629*HEIGHT(M)^2 0.011467*BMI^2 0.072948*HGC(CM)^2 +
0.032936*FAC (CM)^2
RMSE: 13.1
R-Sq: 0.0891,
R-Sq,(Adj)
0.0804
108.31 + -0.0021839* AGE(Y)^2 + 16.753*HEIGHT(M)^2 + 0.032308*BMI^2 + -0.060302
*HGC(CM)^2 + 0.088564*FAC
(CM)^2
RMSE: 23.8
R-Sq: 0.109
R-Sq,(Adj)
0.101
165
Table 5-13 Isometric Endurance Limit Non Linear Regression
Non- Linear Regression Model
Errors
Isometric
Endurance
Limit (20%)
127.25 + -0.0014601 * AGE(Y)^2 + -37.427
*HEIGHT(M)^2 + -0.05763*BMI^2 +
0.084164*HGC(CM)^2 + 0.18183*FAC
(CM)^2
RMSE: 58.4
R-Sq: 0.116
R-Sq,(Adj) 0.107
Isometric
Endurance
Limit (40%)
177.76 -0.0072556* AGE(Y)^2 18.581*HEIGHT(M)^2 -0.035377*BMI^2 0.16351*HGC(CM)^2 + 0.086309*FAC
(CM)^2
RMSE: 33.5
R-Sq: 0.117
R-Sq,(Adj) 0.109
Isometric
Endurance
Limit (60%)
83.723-0.0016898 b2* AGE(Y)^2 8.1407*HEIGHT(M)^2 -0.024757*BMI^2 0.088919*HGC(CM)^2 + 0.05318*FAC
(CM)^2
RMSE: 21
R-Sq: 0.0835,
R-Sq,(Adj) 0.0747
Isometric
Endurance
Limit (80%)
44.498 + 0.0016698* AGE(Y)^2 2.8629*HEIGHT(M)^2 -0.011467*BMI^2 0.072948*HGC(CM)^2 + 0.032936*FAC
(CM)^2
RMSE: 13.1
R-Sq: 0.0891,
R-Sq,(Adj) 0.0804
Isometric
Endurance
Limit (Avg)
108.31 + -0.0021839* AGE(Y)^2 + 16.753*HEIGHT(M)^2 + -0.032308*BMI^2 + 0.060302 *HGC(CM)^2 + 0.088564*FAC
(CM)^2
RMSE: 23.8
R-Sq: 0.109
R-Sq,(Adj) 0.101
166
Table 5-14 Age Effect on Isometric Endurance Limit
actor
Findings
Age
1. Isometric Endurance Limit (20%),
A0: (25-<30) (159.2),A1: (30- <35),(176.4),A3:
(40-<45),(159.73),A2: (35-<40)(163.22)
A4: (45-<50)(177.3),A5 (above 50)(158.9)
2. Isometric Endurance Limit (40%)
A0: (25-<30) (111.1),A1: (30- 35)(86.91),A3: (40
45)(61.27),A2: (35-<40)(76.52),A4:(45<50)(68.07),A5 (above 50)(75.67)
3. Isometric Endurance Limit (60%)
A0: (25-<30)(45.56),A1: (30- <35)(49.45),A3:
(40-45)(32.79),A2: (35-<40)(42.07),A4: (45<50)(34.6),A5 (above 50)(45)
4. Isometric Endurance Limit (80%)
A0: (25-<30) (24.22),A1: (30- <35) (29.3),A3:
(40-<45)(19.05) A2: (35-<40)(21.52),A4: (45<50) (19.65),A0: (25-<30) (30.11)
Conclusion
1. Highest
isometric
mean endurance limit
exerted in A0: (25<30) followed by
30-35 years ago
group and then starts
decreasing by older
ages
2. Highest
isometric
mean endurance limit
(85 Sec) and then
start decreasing by
older ages.
3. Aviation
industry
subjects exerted high
endurance on low
MVC
percentages
than high percentages
Table 5-15 Height effect on Isometric Endurance limit
Factor Findings
Conclusion
Height
Isometric Endurance Limit (20%)
Tall (171),Medium (172.93),Short (153.29)
Isometric Endurance Limit (40%)
1. Limited effect of height
on isometric endurance
limit.
Tall (70.76),Medium (72.72),Short (75.94)
2. Subjects with medium to
tall height exerted higher
endurance limits
Isometric Endurance Limit (60%)
3. Highest
Tall (39.38),Medium (38.06),Short (38.15)
Isometric Endurance Limit (80%)
Tall (21.34),Medium (21.91),Short (21.76)
167
isometric
endurance limit exerted
in (20%) condition.
Table 5-16 BMI Effect on Isometric Endurance Limit
Factor Findings
Conclusion
Height
Isometric Endurance Limit (20%)
Large (174.9),Medium (166.1),Small
(168.67)
Isometric Endurance Limit (40%)
Large (59.12),Medium (73.88), Small
(79.79)
Isometric Endurance Limit (60%)
Large (29.46),Medium (4.383), Small
(40.23)
1.Limited effect of BMI
isometric endurance limit
on
2.Subjects with medium to small
BMI exerted higher endurance
limits average by 8.83% than
those with large BMI
3.Subjects with large BMI values
have lowest isometric endurance
limit.
4. Highest Isometric endurance
limit exerted in (20%) condition
Isometric Endurance Limit (80%)
Large (17.96),Medium (23.84), Small
(21.27)
Table 5-17 Hand Grip Circumference (HGC) Effect on Isometric Endurance Limit
Factor
Findings
Conclusion
Height
Isometric Endurance Limit (20%)
Large (185),Medium (168.66),Small
(149.037)
Isometric Endurance Limit (40%)
Large (60.68),Medium (77.85), Small
(75.15)
Isometric Endurance Limit (60%)
Large (32.97),Medium (41.19), Small
(37.74)
Isometric Endurance Limit (80%)
Large (17.96),Medium (23.28), Small
(23.05)
168
1. Limited effect of HGC on
isometric endurance limit
2. Subjects with medium HGC
have higher endurance limits
average by 4.84% than those
with large HGC and 9.12 than
those with small HGC
3. Small HGC have the weakest
isometric endurance limit
4. Highest isometric endurance
limit
exerted
in
(20%)
condition
5. Final conclusion: subjects with
larger HGC can exerted larger
isometric endurance limit
Table 5-18 Forearm Grip Circumference (FGC) Effect on Isometric Endurance
Limit
Factor Findings
Conclusion
Height
Isometric Endurance Limit (20%)
1.
Subjects with larger FGC
exerted
more
isometric
endurance limit in all
percentages followed medium
and smaller FAC subjects.
2.
Subjects with larger FGC can
exerted
larger
isometric
endurance limit
Large (190.8), Medium
(165.79),Small (151.6)
Isometric Endurance Limit (40%)
Large (74.14), Medium (71.99), Small
(74.62)
Isometric Endurance Limit (60%)
Large (39.96), Medium (37.54), Small
(38.76)
Isometric Endurance Limit (80%)
Large (24.14), Medium (21), Small
(21.32)
Table 5-19 TRADE Effect on Isometric Endurance Limit
Factor Findings
Conclusion
Height
Isometric Endurance Limit (20%)
1. Trade has a major effect on
isometric endurance limit
APG (160.62),COMNAV (138.3 ),E&I
(166.2) ,ENG (200.2) ,GSE(129.7)
2. Highest isometric endurance
limit exerted by APG and
Isometric Endurance Limit (40%)
Engine trades then E& I
APG(87.06 ) , COMNAV(53.88) ,E&I
(56.7),ENG (65.69),GSE (71.80)
Isometric Endurance Limit (60%)
APG (45.62),COMNAV (28.5),E&I
(29),ENG (34.4),GSE (38.13)
Isometric Endurance Limit (80%)
APG(16.86 ),COMNAV (11.2),E&I
(15.25),ENG (8.1) ,GSE (10.11)
169
3. Lowest isometric endurance
limit exerted in COMNAV
Table 5-20 Isometric Endurance Limit for Jordanian Subjects
Variable
Mean
StDev
Minimum
Maximum
Isometric End, Limit (20%)
167.45
3836.74
60.00
343.00
Isometric End, Limit (40%)
73.12
1268.08
21.00
203.00
Isometric End, Limit (60%)
38.37
479.62
9.00
116.00
Isometric End, Limit (80%)
21.75
186.66
5.00
93.00
Table 5-21 Smoking effect on Isometric End, Limit
Factor
Findings
Conclusion
Smokers
Isometric End, Limit (20%) S(176.31)
Isometric End, Limit (40%) S(78.05)
1. Smokers
exerted
more
isometric endurance limit than
non-smokers by 12.98%
Isometric End, Limit (60%) S(40.26)
2. Difference is not high
Isometric End, Limit (80%) S(22.06)
3. Highest exerted in isometric
end, limit (20%).
Non
smokers
Isometric End, Limit (20%) NS(156.14)
Isometric End, Limit (40%) NS(66.83)
4. Reason: Nature of experiment
(low to medium effort) and
56% smokers and younger ages
Isometric End, Limit (60%) NS(35.97)
Isometric End, Limit (80%) NS(21.34
Tables 5-22 through 5-32 shows the independent factors effect and detailed conclusions
for each case (MVC, Isometric fatigue limits and Isotonic fatigue limits)
170
Table 5-22 Hand Dominancy Effect on Isometric End, Limit
Factor
Findings
Conclusion
Dominant
Isometric End, Limit (20%), (166.39)
Isometric End, Limit (40%), (74.01)
Isometric End, Limit (60%) ,(38.85)
Isometric End, Limit (80%) ,22.23)
Non
Dominant
1. Dominant hand exerted
more isometric endurance
mit by 3.57%
2. The
highest
isometric
endurance limit exerted for
dominant hand for age
group
A4:(45-<50)
followed by A2:(35-<40)
3. Lowest isometric end, limit
exerted in A5 (above 50)
and A0 and A0: (25-<30)
IsometricEnd, Limit (20%), (180.4)
IsometricEnd, Limit (40%), (62.3)
IsometricEnd, Limit (60%), (32.5)
IsometricEnd, Limit (80%), (15.9)
Table 5-23 Isotonic Endurance Limit General Linear and Nonlinear Models
Model
Model Summary
Linear
Model
29.495 + 0.15947AGE(Y) -55.559 EIGHT
(M) -0.57916 BMI + 3.558 GC (CM) +
1.1294 FAC (CM)
RMSE: 16.9
R-Sq: 0.0914,
R-Sq,(Adj) 0.0827
Non
Linear
Model
33.635 + 0.0018137* AGE(Y)^2 + 16.255*HEIGHT(M)^2 + -0.010432
*BMI^2 + 0.077773*HGC(CM)^2 +
0.0204*FAC (CM)^2
RMSE: 16.9
R-Sq: 0.0939,
R-Sq,(Adj) 0.0852
171
Table 5-24 Age Effect on Isotonic Endurance limit
Factor
Findings
Age
Conclusion
1.Isoto, End 20-60%,S, RH, A0: (25-<30)
(31.67),A1:(30- 35),(37.55),A3:(40<45),(38.88),A2:(35-<40)(36.63)A4:(45<50)(38.47),A5(above 50)(48.22)
2.Isoto, End, 20-60%,S,RH , A0: (25-<30)
(27.22),A1:(30- 35)(39.45),A3: (4045)(28.3),A2: (35-<40)(32.85),A4: (45<50)(35.56),A5 (above 50)(47)
3.Isoto, End
20-60%,HS, LH A0: (25<30)(28.89),A1: (30- <35)(48.73),A3: (4045)(43.09),A2: (35-<40)(37.3),A4: (45<50)(46.58),A5 (above 50)(40.78).
4.Isoto, End 20-60% LS, LH A0: (25-<30)
(36.64),A1: (30- <35) (28.24),A3: (40<45)(27.63) A2: (35-<40)(33.16),A4:(45<50) (36.22),A0: (25-<30), (30.11)
1- Older subjects aged
above 50 exerted highest
isotonic endurance limit,
followed by subjects aged
between 45 and 50 years
old
2. Subjects of youngest
ages have the lowest
isotonic endurance limit.
3.
Highest
isotonic
endurance limit exerted in
(Isoto, End 20-60% LS,
RH) condition.
Table 5-25 Height Effect on Isometric Endurance Limit
Factor
Findings
Conclusion
Height
Isoto, End 20-60 %( LS, RH)
1. Limited effect of height on
isometric endurance limit.
Tall (35.86),Medium (39.64),Short (37.74)
Isoto, End, 20-60 %( HS,RH)
Tall (31.55),Medium (34.01),Short (35)
Isoto, End, 20-60 %( HS, LH )
Tall (41.14),Medium (45.65),Short (37.08)
2. Subjects of medium height
exerted higher endurance limits
3. Highest isometric endurance
limit was exerted in (20%)
condition.
Isoto, End 20-60 %( LS, LH)
Tall (27.28),Medium (32.54),Short (29.76)
Table 5-26 BMI Effect on Isometric Endurance Limit
Factor
Findings
Conclusion
Height
Isoto, End 20-60 %( LS, RH)
Large (43.35),Medium (36.62),Small
(37.65)
Isoto, End, 20-60 %( HS, RH)
Large (37.69),Medium (32.31), Small
(33.29)
1.Limited effect of BMI on
isometric endurance limit.
2.Subjects with larger BMI
exerted higher isotonic
endurance limits.
3. Highest
172
isometric
endurance limit was exerted
in
Isoto,
End,
2060%(HS,LH) condition.
Isoto, End, 20-60 %( HS, LH)
Large (46.96),Medium (40.53), Small
(42.33)
Isoto, End 20-60 %( LS, LH)
Large (30.31),Medium (34.95), Small
(36.13)
Table 5-27 Hand Grip Circumference (HGC) Effect on Isom, End, and Limit
Factor Findings
Conclusion
Height
1. Larger HGC subjects exerted
more isotonic endurance limit
than subjects with other HGCs
Isoto, End 20-60 %( LS, RH)
Large (40.45), Medium (38.31),Small
(36.38)
Isoto, End, 20-60 %( HS, RH)
2. Highest isotonic endurance limit
Large (39.65), Medium (32.54), Small
was exerted in Isoto, End, 20-60
(30.68)
%(HS, LH)
Isoto, End, 20-60 %( HS, LH)
Large (55.48), Medium (40.94), Small
(33.55)
Isoto, End 20-60 %( LS, LH)
Large (34.35), Medium (31.64), Small
(25.38)
Table 5-28 Forearm Effect on Isometric Endurance limit
Factor Findings
Conclusion
Height Isoto, End 20-60 %( LS, RH)
Large (40),Medium (38.031),Small (37.53)
Isoto, End, 20-60 %( HS, RH)
Large (41.5),Medium (31.27), Small (32.38)
2. Highest isotonic endurance
limit is exerted in Isoto,
End, 20-60 %( HS, LH)
Isoto, End, 20-60 %( HS, LH)
Large (50.57),Medium (40.33), Small
(40.15)
Isoto, End 20-60 %( LS, LH)
Large (34.93),Medium (28.67),
(31.26)
173
1. Larger
FAC
subjects
exerted more isotonic
endurance
limit
than
subjects with other FACs
Small
Table 5-29 Trade Effect on Isometric Endurance Limit
Facto Findings
Conclusion
r
Heigh Isoto, End 20-60 %( LS, RH)
1. Trade has a major effect
t
APG (39.68),COMNAV (37.19),E&I (41) ,ENG
on isometric endurance
(39.17) ,GSE (31.27)
limit
Isoto, End, 20-60 %( HS, RH)
APG(34.09 ) , COMNAV (26.56)
,E&I(29.17),ENG (38.1),GSE (29.67)
Isoto, End, 20-60 %( HS, LH)
APG (36),COMNAV (35.81),E&I (46.83),ENG
(57.02),GSE (29.8)
Isoto, End 20-60 %( LS, LH)
APG(28.74 ),COMNAV(23.19),E&I(38.3),ENG
(36.29) ,GSE(26.67)
2. Highest on isometric
endurance limit was
exerted by Engine than
Electrical& Instrument
trade
3. Highest isometric
endurance limit exerted
Isoto, End, 20-60 %(
HS, LH) condition
4. Engine and E& I have
the mean ages of 42 and
37, respectively
Table 5-30 Isometric Endurance Limit for Jordanian Subjects
Variable
Mean
StDev
Minimum
38.32
15.72
6.00
Isoto, End 20-60 %(
LS, RH)
Maximum
110.00
Isoto, End, 20-60 %(
HS, RH)
33.73
15.18
9.00
80.00
Isoto, End, 20-60 %(
HS, LH)
42.45
22.33
9.00
109.00
Isoto, End 20-60 %(
LS, LH)
30.67
13.99
7.00
85.00
174
Table 5-31 Smoking Effect on Isometric Endurance Limit
Factor
Findings
Conclusion
Smokers Isoto, End 20-60 %( LS,
1. Smokers exerted more isotonic
RH)S(37.91)
endurance limit than non-smokers
by 1.85%.
Isoto, End, 20-60 %( HS,
RH)S(34.2)
2. Highest exerted in isoto, end, 20Isoto, End, 20-60 %( HS,
LH)S(43.15)
60 % (HS, LH).
Isoto, End 20-60 %( LS,
LH)S(31.08)
3. Reason: nature of experiment (low
to medium effort) and 56%
Non
Isoto, End 20-60 %( LS,
smokers and younger ages.
smokers
RH)NS(38.84)
Isoto, End, 20-60 %( HS,
RH)NS(33.12)
Isoto, End, 20-60 %( HS,
LH)NS(41.57)
Isoto, End 20-60 %( LS,
LH)NS(30.14)
Table 5-32 Dominancy Effect on Isotonic Endurance Limit
Factor
Findings
Conclusion
Dominant
Non
Dominant
Isoto, End 20-60 %( LS, RH)
(38.39)
Isoto, End, 20-60 %( HS, RH)
(34.12)
Isoto, End, 20-60 %( HS, LH)
(41.53)
Isoto, End 20-60 %( LS, LH)
(30.66)
Isoto, End 20-60 %( LS, RH)
(37.40)
Isoto, End, 20-60 %( HS, RH)
(28.9)
Isoto, End, 20-60 %( HS, LH)
(53.7)
Isoto, End 20-60 %( LS, LH)
(30.70)
175
1. There is almost no effect for
dominancy
on
isotonic
endurance limit.
2. Highest isotonic endurance
limit is exerted in Isoto, end,
20-60 %( HS, LH).
5.2 NEURAL NETWORK ANALYSIS CONCLUSION
Mean square errors (MSE) and R values for the neural network model are shown in Table
5-33 for the MVC, isometric and isotonic endurance limits. Results showed that the
neural network model provided good performance.
Table 5-33 Neural Network Summary (MVC, Isometric and Isotonic Endurance
Limits)
MVC
Isometric Endurance Isotonic Endurance Limit
Limit
MSE
R
MSE
R
MSE
R
7.09 e -8
9.9 e-1
3.35 e-7
9.9 e-1
1.2 e-3
9.9 e-1
1.56 e-7
7.51 e-8
9.9 e-1
9.9 e-1
3.4 e-7
2.54 e-7
9.9 e-1
9.9 e-1
6.5 e-4
2.4 e-3
9.9 e-1
9.9 e-1
Neural network performance plots are shown in Table 5-1, for the three datasets (training,
validation and testing). Validation performance was shown in Table 4-34 where best
validation performance was at 1.5 e-7 at epoch 554 for the MVC test and 3.41 e-7 at
epoch 1000 for the isometric endurance limit test and .0000655 at epoch 16 for the
isotonic endurance limit test. In this research, all results are reasonable since the final
MSEs are very small. The testing and validations errors are similar and no significant
over fitting has occurred.
In the experiments, the most errors are near zero, as viewed for the three tests (MVC,
isometric and isotonic endurance limits). The error bars is very little for all three tests:
MVC, isometric and isotonic tests. Results show that the neural network has learned and
fitted the experiment data well. The neural network model outputs accurately resemble
the experiment targets for the three datasets (training, testing, and validation).
176
5.3 ANFIS NEURAL NETWORK ANALYSIS CONCLUSION
By examining the output checking error sequences over the whole training period, it is
clear that the experiment checking dataset is very good for model validation and achieves
minimum checking error. Also, step-size errors show very small numbers which serves to
adjust references for the initial step-size and increasing and decreasing rates. Table 5-34
shows ANFIS Output Errors for the Tests (MVC, Isometric and Isotonic Endurance
Limits) and table 5-35 shows ANFIS Output Errors for Each Experimental Condition
Table 5-34 ANFIS Output Errors for the Tests (MVC, Isometric and Isotonic
Endurance Limits)
Test Results
Error
MVC
3.73432 e-3 step size (0.005905)
Isometric Endurance Limits
4.2323e-05 Step size (0.008100)
Isotonic Endurance Limits
3.6203e-05 (0.006561)
Table 5-35 ANFIS Output Errors for Each Experimental Condition
Test Results
Error
MVC(Kg, Sit, D)
3.84522e-05
MVC(Kg, Sit, ND)
2.46537e-05
MVC(Kg, Stand, D)
3.6203e-05
MVC(Kg, Stand, ND)
1.56111e-05
Isometric End, Limit (20%)
0.000128428
Isometric End, Limit (40%)
5.33146e-05
Isometric End, Limit (60%)
2.26027e-05
Isometric End, Limit (80%)
3.80123e-05
Isoto, End 20-60% low, SP, RH
3.00345e-05
Isoto, End, 20-60% High, SP, RH
1.73763e-05
Isoto, End, 20-60% High, SP, LH
4.6505e-05
Isoto, End, 20-60% low, SP, LH
4.61178e-05
177
5.4 Future Work
This research considers all parameters that affect the MVC, isometric and isotonic
fatigue. It has an increased importance in all aspects of job design, ergonomics and health
care research. This research recommends conducting more future studies where more
races could be included in the experiments since the literature showed great mean
differences in MVC regarding different races. For example, repeating the study using
subjects from different races could further investigate the effects of race. Additionally,
aviation female subjects could be included. Increasing the sample size might allow us to
draw a more definitive conclusion. One could also study the relationship between
subjects’ MVC and survival rates from (1) cancer, or (2) chronic kidney disease.
Similarly, the relationship between subjects’ MVC and dementia progression or walking
speed could also be studied. Future studies can include the effects of nutritional status
and bone mineral content on MVC and endurance. Also, new experiments should
consider using the new digital and computerized grip strength measurement apparatus
(e.g., grip strength reader). In addition, one could design new apparatus that measure
actual MVC in a different way than dynamometer where all independent factors can
provide more realistic measurements. Future studies could be conducted to evaluate the
palm reflexology hand therapy and include a pinch grip where researchers can better
correlate diseases with max MVC.
New and important trades should be included. For health care applications, surgery
doctors and nurses in hospitals could be studied for the effects on their performance
accuracy during operations. For engineering applications, one could recruit special
welding technicians (argon welding) as subjects to study the direct effects on their
178
performance accuracy during operations. Finally, a large number of subjects can be
studied for “strength and quality of life among critical patients, and population aging”
(Sirajudeen et al. 2012).
179
Appendices
180
APPENDIX A ANTHROPOMETRIC DATA
Gen
Age
Trade
Smoking
Weight
(Kg )
Height
(Cm)
Hand Grip
Circumference
(CM)
Forearm
Circumference
(CM)
Hand
Dom
S, NS
Weight
Height
HGC
FAC
D, ND
M,F
1
M
60
APG
Eng
E&I
COMN
AV
Avionic
S
90
180
24
31
d
2
M
33
ENG.
S
81
181
24
30
D
3
M
49
GS
NS
79
193
22.5
29
D
4
M
47
ENG.
NS
89
175
23.5
29
ND
5
M
56
Airframe
s
97
170
21.5
30
d
6
M
44
Airframe
S
75
165
22
27
d
7
M
65
Airframe
s
80
168
23.5
30
d
8
M
39
Avionic
NS
82
172
21.5
27
D
9
M
52
ENG.
S
83
183
24.5
31.5
D
10
M
53
Airframe
NS
80
176
22.5
28.5
d
11
M
43
Avionic
NS
78
168
22
28
d
12
M
41
Airframe
S
65
165
21
29
D
13
M
48
ENG.
S
87
183
24.5
33.5
D
14
M
39
comnav
NS
68
175
21.5
25.5
d
15
M
45
Airframe
NS
73
173
21
26
d
16
M
41
E&I
NS
95
183
22.5
34
D
17
M
44
ENG.
s
110
182
25.5
35
D
18
M
32
S
68
168
20.5
27
D
19
M
36
Ground
support
ENG.
s
59
167
21.5
25
d
20
M
44
Airframe
S
92
190
22
29
D
21
M
47
GS
NS
83
170
22
28
D
22
M
39
ENG.
S
77
173
24
31
D
23
M
50
Airframe
NS
78
175
22
26.5
d
24
M
50
Airframe
s
69
165
20
28
d
25
M
38
APG
NS
70
182
21
26
d
26
M
48
ENG.
NS
85
175
24
29.5
D
27
M
43
GS
NS
75
186
22.5
32.5
D
28
M
36
NDI
S
88
179
22
28
D
29
M
29
Avionic
NS
78
173
19.5
28
D
30
M
43
ENG.
NS
68
170
22.5
26.5
D
31
M
47
GS
NS
90
185
23.5
31.5
D
32
M
47
APG
S
81
168
22
30.5
D
33
M
37
ENG.
NS
70
177
23.5
29.5
ND
34
M
49
APG
S
95
185
24.5
29
D
35
M
46
ENG.
S
100
178
24
33
D
36
M
52
Airframe
s
68
173
22
29
d
37
M
37
ENG.
S
60
168
20
25.5
D
181
38
M
46
39
M
46
40
M
36
41
M
43
42
M
43
M
44
ENG.
S
105
185
24
35
D
GS
S
76
171
22
30.5
D
E&I
NS
78
180
22
25.5
d
NS
73
167
23
30
d
50
Simulato
r
ENG.
NS
84
187
22.5
29
ND
38
Airframe
s
83
178
21
27.5
d
M
59
Airframe
NS
100
170
21.5
29.5
d
45
M
40
ENG.
s
90
185
22
29
D
46
M
40
APG
NS
75
170
24
31
D
47
M
42
Airframe
s
83
187
22.5
32
D
48
M
44
Airframe
S
105
180
24
31
d
49
M
50
Airframe
NS
70
170
21
27
d
50
M
48
APG
NS
92
175
22
31
D
51
M
30
APG
S
61
170
20.5
26.5
D
52
M
41
Avionic
S
58
160
21
25
D
53
M
23
Airframe
S
70
178
23
27
D
54
M
35
Avionic
NS
90
187
23
30
D
55
M
47
ENG.
S
87
178
22
31
D
56
M
43
APG
S
112
173
24.5
33
D
57
M
50
Airframe
NS
88
178
21.5
28
d
58
M
45
ENG.
S
107
181
25
35
D
59
M
46
GS
S
83
170
23
29
D
60
M
34
ENG.
NS
83
179
22
29
D
61
M
35
E&I
NS
90
170
23
28
d
62
M
37
ENG.
s
82
179
23
27
ND
63
M
45
ENG.
S
63
173
22.5
26.5
ND
64
M
36
Airframe
NS
86
171
21
28.5
d
65
M
35
ENG.
S
75
174
23.5
29.5
D
66
M
34
ENG.
NS
86
178
24
31.5
D
67
M
44
ENG.
S
77
185
25.5
30
D
68
M
42
Airframe
NS
82
176
22.5
29
D
69
M
34
Avionic
S
72
165
21
27
D
70
M
46
ENG.
NS
92
180
24.5
33.5
D
71
M
37
ENG.
S
74
176
22.5
27
d
72
M
39
comnav
NS
70
177
23.5
27.5
d
73
M
26
Airframe
S
55
165
21
24
D
74
M
63
Avionic
NS
96
172
22.5
29
D
75
M
30
Airframe
S
80
165
22
29
D
76
M
43
NDI
S
75
165
20
30
d
77
M
31
GS
S
94
178
23
32.5
D
78
M
23
Airframe
NS
72
173
21
29
D
79
M
50
Airframe
NS
80
178
22
28.5
d
80
M
37
APG
s
82
185
23
28
d
81
M
36
ARMT
s
67
170
20
25.5
d
82
M
39
E&I
NS
99
175
22
30
D
83
M
36
NDI
s
96
184
23
29
ND
84
M
43
E&I
NS
89
173
23
27
d
85
M
53
Airframe
S
77
171
22
30
D
86
M
41
GS
NS
74
155
22
28
D
87
M
35
comnav
NS
93
175
24
30
d
182
88
M
48
ENG.
NS
65
165
22
27.5
D
89
M
35
Airframe
S
82
180
90
M
44
GS
S
95
170
21
29
D
24
31.5
91
M
32
E&I
S
90
D
178
21
30.5
92
M
43
Airframe
s
D
85
170
22
30
93
M
39
comnav
D
S
102
182
23.5
30
94
M
49
D
ENG.
S
84
178
23.5
29
D
95
M
96
M
23
Airframe
NS
73
180
22.5
30
d
42
ENG.
S
82
182
25.5
30
D
97
M
98
M
36
NDI
S
95
171
22
33
d
38
comnav
S
103
185
24
32
D
99
M
38
ENG.
S
70
172
23
28
D
100
M
49
ENG.
NS
86
171
21.5
29.5
ND
101
M
29
Airframe
NS
84
180
21.5
29
d
102
M
37
Airframe
NS
78
169
21
26
D
103
M
47
ENG.
S
99
184
24.5
35
D
104
M
40
ENG.
NS
85
185
22.5
30
D
105
M
37
ENG.
S
65
175
23
29
D
106
M
50
ENG.
NS
88
180
25
31.5
D
107
M
30
GS
NS
61
171
22
23
d
108
M
49
Airframe
S
74
177
22
27
D
109
M
39
GS
s
95
188
23
33
D
110
M
39
GS
NS
68
178
21.5
25
D
111
M
26
Airframe
S
94
180
21.5
29.5
d
112
M
24
Airframe
S
74
173
22
28
D
113
M
38
Comnav
NS
114
193
24
31
d
114
M
46
ENG.
NS
95
173
22.5
31
ND
115
M
38
Airframe
S
105
183
23
31.5
d
116
M
47
ENG.
NS
100
170
22.5
31.5
D
117
M
48
ENG.
S
98
181
23
33
ND
118
M
42
Airframe
S
73
176
20.5
27
d
119
M
45
Airframe
NS
67
170
22
29
d
120
M
47
Airframe
s
81
181
24
29.5
d
121
M
48
ENG.
S
88
188
24.5
27
D
122
M
29
ENG.
S
56
173
20.5
26.5
D
123
M
45
GS
S
81
183
22
31
D
124
M
45
GS
NS
94
175
24
31.5
D
125
M
42
ENG.
NS
107
181
24.5
33
D
126
M
47
ENG.
S
64
165
22
27.5
D
127
M
49
Airframe
NS
100
179
24
33
d
128
M
46
ENG.
NS
90
172
24
29.5
ND
129
M
48
Airframe
S
60
160
20
28
d
130
M
34
Airframe
NS
85
176
22
32
d
131
M
27
Airframe
S
95
184
24.5
34
D
132
M
50
Airframe
s
76
174
20
27.5
d
183
APPENDIX B: MVC DATA
#
Max (MVC)Kg
Max (MVC)Kg
Max (MVC)Kg
Max (MVC)Kg
MAX(MVC)
SITTING,
Right Hand
MAX(MVC)
SITTING,
Left Hand
MAX(MVC)
STANDING,
Right Hand
MAX(MVC)
STANDING,
Left Hand
1
2
3
4
5
6
7
8
9
51
61.1
45.8
43.2
24.6
37.7
37.5
45.2
47.9
50
60.5
41.3
46.1
28
35.3
35.7
40
43.6
53
57.4
44.5
43.6
24.8
36.5
39.6
47
51.7
46
59.1
17.1
43.2
21.6
34
36
40.9
45.6
10
11
12
13
46.3
45
37.6
46.6
43.4
43.6
34.4
50.7
47
48.2
43.7
54.6
46.5
46.3
37.4
49.9
14
15
16
17
18
19
20
21
22
23
24
25
26
40.1
46
55.2
56.8
48
38.2
59.3
47
49.9
38.9
40
52.3
41
38.2
41.8
47.3
43
47.7
34.7
54.7
50
46.3
34.5
50
44.2
46.4
41.2
48
58.8
60.8
53
36.8
61.2
41
57.7
38
43.3
54.7
38.9
37.8
48.9
49.6
47.4
42.8
39.1
60.3
45
48.7
32.5
40.4
46.5
44.2
27
28
29
30
31
32
33
55.3
43.7
43.7
40.6
51.3
41.1
55.6
54.5
45.8
45
34
52.3
45.8
57.7
54.3
40.8
47.2
38.9
59.1
44.7
52.1
50.3
40.9
42.7
31
54.3
42.3
51.3
34
35
36
37
38
39
40
46.5
55.7
45
43
54.9
39.2
40
46.4
54.4
40
38.2
41.4
44.2
38.5
42.7
60.9
50.1
37.6
59
42.9
38.5
45.8
56.8
44.1
34.2
45.3
40.2
34.5
184
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
51.7
47.7
50.3
33
51.6
51.8
49.2
48.1
40.2
45
52.4
41
37.5
50
43.1
44.8
47
58.9
33.1
45
44.4
42
38.7
46.5
42.9
43
29
50
47.9
45.1
42.5
37.3
46
57.9
35.4
48.8
45.8
44.4
50.7
34
54.5
39.6
51.6
44.7
38.2
32.4
55.1
46.3
49.3
35
53.2
59.5
49.4
54
40.4
48.3
51.4
44.7
42.5
54.1
46.5
48.3
44.3
66.3
49.3
48.4
50.3
43.4
37.1
22.8
19.2
41.9
31
51.1
50.8
45.1
47.2
33.2
49.5
52.1
41.5
47
47.7
47.4
48.7
37.7
56.2
47.2
55.1
45.5
37.7
28.9
64
65
66
67
35
57.5
63
42.7
39
59.3
62.1
38.2
41
53.9
59.2
42.4
39
53.4
61.2
38.3
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
53.7
40.1
48.5
46.8
52.4
36.5
29.8
44.6
50.9
47.5
53.9
43
48.7
42.7
48.4
57
49.9
37.5
39.2
53.4
42.9
55.5
54.7
50.2
56
38.1
52.3
42
58
36.7
27
42.3
54.5
45
51.9
43
47.6
40.6
42
45
43.7
34.5
35.5
51.6
39.7
51.6
55.1
51.3
57.8
41.7
56.4
46
51
42.6
29.7
47.4
51
54.9
54.8
43
57.2
43.3
50.1
57
50.9
35.7
37.2
49.1
46.8
49.9
46.6
50
57.4
33.4
52
37
54.2
39.5
27
43.7
38.4
45.3
51
45
49.8
38.7
44.8
36.4
46.4
35.1
41.3
50
41.5
53.4
55.6
55.2
185
92
93
94
45
51.2
41.3
40.2
44.3
44.5
46.2
52.7
41.8
45.9
46.6
41.1
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
57.7
44.6
55
51.4
46.3
44
51.6
44.5
57
54.7
44.4
49.8
38
38
60.1
39.9
45
43.3
67.6
35.2
68
37.1
53.8
61.4
39.8
56
49.5
45
43.6
50.2
46.8
52.9
50.4
43.4
45.2
40.8
42.3
58.9
35.9
48
58.5
68
35.9
64.6
37.5
52.8
59.3
44.2
49
48.5
51.7
45.2
49.6
45.5
64.5
60.3
49.9
53.5
39.4
39
61
40.8
49
55.2
61.5
36.8
60
38.6
59.1
57
40.4
53
47.3
49.4
45
48
47.1
54.1
60.1
47.3
47.7
39.7
44.3
60.6
31
47.6
49.6
64.9
42.1
49
44.2
54.7
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
57
58
55.5
44.6
50.5
54.1
53.9
42.9
41
44.5
42.9
30
52
81.6
54
50.8
55.3
43.9
44.8
56.3
53.2
48
49.1
38.1
46.5
48
32
52
77.8
50.4
60.5
61
55
40.9
49.6
50.2
63
46.5
45
36
40.7
37
56
78.3
49
48
51.4
46
43.7
50
50.1
44
46.6
39.4
45
46.3
38
51.8
68.2
42
186
APPENDIX C: ISOTROMIC ENDURANCE LIMIT MVC DATA
#
20%
MVC
MAX (Isometric
Endurance
Limit
(Total) (Sec)
40%
MVC
TIME 20%
MVC
MAX
(Isometric
Endurance
Limit
(Total) (Sec)
60%
MVC
TIME 40%
MVC
MAX (Isometric
Endurance Limit
(Total) (Sec)
80%
MVC
TIME 60%
MVC
MAX(Isometri
c Endurance
Limit
(Total) (Sec)
TIME 80%
MVC
10.2
115
20.4
56
30.6
23
40.8
16
12.22
343
24.44
69
36.66
60
48.88
18
9.16
75
18.32
31
27.48
16
36.64
9
8.64
300
17.28
53
25.92
25
34.56
14
4.92
187
9.84
96
14.76
43
19.68
13
7.54
90
15.08
32
22.62
18
30.16
13
7.5
187
15
122
22.5
100
30
93
9.04
125
18.08
22
27.12
18
36.16
7
9.58
118
19.16
54
28.74
30
38.32
23
10
11
12
13
9.26
190
18.52
66
27.78
21
37.04
12
9
88
18
50
27
15
36
9
7.52
191
15.04
67
22.56
22
30.08
17
9.32
293
18.64
83
27.96
28
37.28
22
14
15
16
17
18
19
20
21
22
23
24
25
26
8.02
90
16.04
29
24.06
23
32.08
8
9.2
227
18.4
112
27.6
57
36.8
41
11.04
211
22.08
102
33.12
67
44.16
45
11.36
240
22.72
58
34.08
27
45.44
20
9.6
199
19.2
133
28.8
40
38.4
33
7.64
183
15.28
37
22.92
13
30.56
5
11.86
189
23.72
81
35.58
68
47.44
26
9.4
113
18.8
73
28.2
32
37.6
20
9.98
233
19.96
53
29.94
30
39.92
18
7.78
145
15.56
98
23.34
33
31.12
19
8
230
16
75
24
35
32
24
10.46
220
20.92
39
31.38
19
41.84
11
8.2
136
16.4
51
24.6
32
32.8
15
11.06
180
22.12
92
33.18
43
44.24
29
8.74
98
17.48
59
26.22
16
34.96
11
8.74
200
17.48
116
26.22
65
34.96
22
8.12
180
16.24
54
24.36
50
32.48
15
10.26
93
20.52
72
30.78
39
41.04
25
8.22
180
16.44
55
24.66
31
32.88
18
11.12
216
22.24
45
33.36
18
44.48
10
1
2
3
4
5
6
7
8
9
27
28
29
30
31
32
33
34
35
36
37
38
39
9.3
100
18.6
36
27.9
17
37.2
12
11.14
314
22.28
35
33.42
12
44.56
15
9
200
18
90
27
73
36
29
8.6
139
17.2
112
25.8
34
34.4
12
10.98
253
21.96
67
32.94
34
43.92
23
7.84
193
15.68
64
23.52
38
31.36
21
187
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
8
180
16
42
24
12
32
9
10.34
75
20.68
45
31.02
15
41.36
14
9.54
62
19.08
22
28.62
9
38.16
6
10.06
128
20.12
23
30.18
18
40.24
9
6.6
99
13.2
21
19.8
12
26.4
11
10.32
135
20.64
79
30.96
49
41.28
40
10.36
220
20.72
44
31.08
23
41.44
15
9.84
179
19.68
96
29.52
52
39.36
29
9.62
123
19.24
97
28.86
30
38.48
21
8.04
180
16.08
89
24.12
40
32.16
23
9
185
18
27
27
20
36
13
10.48
80
20.96
47
31.44
19
41.92
11
8.2
121
16.4
99
24.6
49
32.8
31
7.5
160
15
101
22.5
39
30
37
10
153
20
89
30
53
40
35
8.62
198
17.24
36
25.86
27
34.48
16
8.96
186
17.92
81
26.88
30
35.84
12
9.4
150
18.8
54
28.2
22
37.6
14
11.78
190
23.56
64
35.34
31
47.12
20
6.62
97
13.24
39
19.86
27
26.48
16
9
130
18
83
27
54
36
26
8.88
190
17.76
27
26.64
19
35.52
10
8.4
81
16.8
35
25.2
12
33.6
8
7.74
193
15.48
63
23.22
57
30.96
18
47
7
126
14
98
21
80
28
11.5
203
23
36
34.5
11
46
7
12.6
330
25.2
60
37.8
53
50.4
15
8.54
193
17.08
42
25.62
29
34.16
14
10.74
172
21.48
109
32.22
41
42.96
18
8.02
176
16.04
70
24.06
41
32.08
35
9.7
280
19.4
74
29.1
21
38.8
19
9.36
300
18.72
203
28.08
99
37.44
45
10.48
112
20.96
29
31.44
20
41.92
7
7.3
129
14.6
99
21.9
42
29.2
15
5.96
219
11.92
87
17.88
39
23.84
35
8.92
188
17.84
111
26.76
35
35.68
15
10.18
200
20.36
90
30.54
60
40.72
22
9.5
76
19
72
28.5
32
38
27
10.78
113
21.56
91
32.34
47
43.12
28
8.6
120
17.2
79
25.8
39
34.4
17
9.74
112
19.48
52
29.22
17
38.96
10
8.54
86
17.08
44
25.62
14
34.16
10
9.68
165
19.36
28
29.04
20
38.72
8
11.4
223
22.8
190
34.2
97
45.6
33
9.98
116
19.96
36
29.94
12
39.92
6
7.5
112
15
89
22.5
64
30
39
7.84
189
15.68
102
23.52
48
31.36
33
10.68
156
21.36
25
32.04
20
42.72
6
8.58
240
17.16
140
25.74
70
34.32
11
11.1
99
22.2
45
33.3
44
44.4
43.8
10.94
102
21.88
44
32.82
31
43.76
16
188
91
92
93
94
10.04
135
20.08
105
30.12
44
40.16
25
21
9
121
18
91
27
58
36
10.24
187
20.48
31
30.72
21
40.96
8
8.26
313
16.52
62
24.78
32
33.04
17
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
11.54
177
23.08
143
34.62
54
46.16
44
8.92
180
17.84
33
26.76
22
35.68
11
11
176
22
112
33
78
44
50
10.28
170
20.56
42
30.84
22
41.12
11
9.26
180
18.52
60
27.78
19
37.04
12
8.8
90
17.6
56
26.4
23
35.2
12
10.32
140
20.64
103
30.96
37
41.28
28
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
8.9
86
17.8
76
26.7
70
35.6
63
11.4
203
22.8
73
34.2
38
45.6
23
10.94
187
21.88
90
32.82
66
43.76
32
8.88
193
17.76
69
26.64
26
35.52
15
9.96
105
19.92
45
29.88
23
39.84
20
7.6
93
15.2
87
22.8
69
30.4
50
7.6
99
15.2
80
22.8
39
30.4
25
12.02
203
24.04
87
36.06
50
48.08
33
7.98
100
15.96
54
23.94
41
31.92
35
9
211
18
115
27
26
36
12
8.66
210
17.32
176
25.98
74
34.64
18
13.52
139
27.04
28
40.56
18
54.08
10
7.04
189
14.08
73
21.12
40
28.16
28
13.6
219
27.2
101
40.8
30
54.4
22
7.42
176
14.84
64
22.26
33
29.68
25
10.76
327
21.52
44
32.28
19
43.04
18
11.4
183
22.8
112
34.2
58
45.6
40
11.6
139
23.2
105
34.8
45
46.4
25
11.1
250
22.2
145
33.3
90
44.4
43
8.92
113
17.84
45
26.76
24
35.68
15
10.1
93
20.2
56
30.3
26
40.4
14
10.82
172
21.64
87
32.46
41
43.28
25
10.78
60
21.56
40
32.34
25
43.12
18
8.58
199
17.16
90
25.74
37
34.32
15
8.2
253
16.4
149
24.6
77
32.8
14
8.9
112
17.8
55
26.7
21
35.6
12
8.58
123
17.16
42
25.74
25
34.32
12
6
95
12
80
18
36
24
32
10.4
190
20.8
119
31.2
97
41.6
72
16.32
157
32.64
150
48.96
116
65.28
17
10.8
256
21.6
90
32.4
65
43.2
30
189
APPENDIX D ISOTONIC MVC DATA
MAX(Isotonic
Muscle Fatigue Test
(Total) (Sec)
MAX(Isotonic Muscle
Fatigue Test (Total) (Sec)
MAX(Isotonic Muscle Fatigue
Test (Total) (Sec)
MAX(Isotonic
Muscle Fatigue
Test (Total)
(Sec)
20%-60% low Right
20%-60% high Right
20%-60% low left
20%-60% high
left
1
2
3
4
5
6
7
8
9
19
45
13
45
33
32
67
40
37
15
71
18
14
30
31
69
24
52
12
109
13
98
31
39
59
40
48
9
65
11
57
29
37
41
21
47
10
11
12
13
56
29
25
42
51
23
27
40
40
23
21
67
38
20
13
34
14
15
16
17
18
19
20
21
22
23
24
25
26
60
42
44
49
40
26
27
20
60
39
85
52
42
30
43
41
70
39
28
29
20
47
33
80
30
32
53
21
45
60
39
45
28
18
63
27
18.8
58
70
28
20
47
30
42
24
29
15
36
25
26
28
36
27
28
29
30
31
32
33
29
22
29
41
35
29
36
24
17
29
28
32
30
37
22
31
30
34
39
62
90
17
20
23
27
36
45
34
190
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
29
41
92
13
56
36
28
25
6
41
39
37
53
28
47
110
49
21
45
50
33
56
44
21
35
20
39
45
27
48
21
35
80
11
77
37
26
19
11
26
31
31
40
31
43
75
24
17
40
47
30
31
51
27
48
22
40
27
22
35
45
88
60
9
64
66
80
27
9
46
30
30
59
29
39
99
42
60
46
43
33
46
67
20
75
21
29
43
32
38
34
42
55
9
33
48
85
19
8
22
21
24
33
23
33
71
35
28
39
39
24
38
40
23
56
16
26
26
21
30
64
65
66
67
22
29
38
47
19
30
64
28
19
86
105
56
15
31
62
26
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
18
30
35
12
38
20
40
36
43
31
34
23
58
38
49
32
56
19
23
33
11
25
19
42
31
40
30
32
21
18
24
25
23
32
13
29
63
10
29
17
38
29
40
25
28
31
75
36
42
29
50
12
28
31
9
21
19
30
31
34
24
29
21
34
22
20
21
35
191
85
86
87
88
89
90
91
92
93
94
51
26
42
18
50
40
24
32
54
52
53
29
26
56
41
31
24
28
27
21
49
30
40
40
32
25
21
31
46
102
56
31
25
43
34
21
17
33
23
60
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
40
40
49
41
48
23
30
59
42
31
55
30
50
37
31
42
10
44
32
74
19
67
48
29
21
40
24
33
19
24
58
55
29
40
45
45
38
32
41
9
32
24
61
18
54
42
26
52
37
43
45
15
21
50
79
27
49
44
41
31
25
40
9
31
48
68
18
64
92
29
23
33
24
49
11
17
44
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