Journal of Advances in Information Technology Vol. 6, No. 4, November 2015
Knowledge-Based System Framework for
Training Long Jump Athletes Using Action
Recognition
T. Kamnardsiri 1, W. Janchai 1, P. Khuwuthyakorn 1, P. Suwansrikham 1, and J. Klaphajone
2
1
2
College of Arts, Media and Technology, Chiang Mai University
Department of Rehabilitation Medicine, Faculty of Medicine, Chiang Mai University
Email: {teerawat.k, worawit.j, pattaraporn.k, parinya.sg, jakkrit.k}@cmu.ac.th
P. Suriyachan
Faculty of Education, Chiang Mai Rajabath University
[email protected]
Abstract—The long jump is a part of track and field event.
Also, it has been a standard event in modern Olympic Games.
Athletes have to use their strength, skills and effort to make
distance as far as possible from a jumping point. The long
jump consists of 4 phases: run phase, take-off phase, flight
phase, and landing phase. The actions in each phase effect to
the flight distance. If athletes perform right actions in each
phase, their performance will be significantly improved
prior. In order to have right actions, they need coaching
from experts. However, due to the lack of experts in the field,
coaching the right actions con not be proceeded widely. In
this paper, we propose a framework of an expert system for
training long jump athletes by combing computer vision
techniques and knowledge management theory. The expert
system will capture and learn the right actions of the long
jump experts in each phase. Then it will be able to analyse
and coach learner/jumper based on knowledge captured
from the experts.
Index Terms—action recognition, expert system, long jump,
computer vision, suggestion system, knowledge-based system
I.
INTRODUCTION
The long jump was a section of the first Olympics in
ancient Greece, circa 708 B.C., It had first become in the
Olympic sport since 1896. It has been included in track
and field of the athletic sports on the local, national and
also international levels. Long jumpers generally practice
following the long jump principle techniques such as
approach running, taking-off, flighting and landing to
achieve in maximum performance. Until, the world record
of the long jump distance for man stands at 8.95 m. The
record has been broken by Mike Powell of the United
States. The world record set held in Tokyo (1991).
Furthermore, since 1988 the women record was held by
Galina Chistyakova of the Soviet at 7.52 m [1].

Manuscript received April 7, 2015; revised August 2, 2015.
© 2015 J. Adv. Inf. Technol.
doi: 10.12720/jait.6.4.182-193
182
The achievement of long jumping is attempt to jump as
far as possible. The long jump which comprise 4 phases
are the approach run phase, the take-off on the wood board
phase, the flight phase and the landing phase. Furthermore,
in order to make the flight distance, the biomechanical
parameters of the long jump have been determined by
Take-off speed, Take-off angle, Take-off height and
Aerodynamics [2]. Moreover, Linthorne (2008) addresses
that biomechanical principles of the long jump are the
run-up, take-off, flight, and landing phases. These
principles are behind the successful for the long distance
long jumps [3]. Therefore, long jump performance is
considerable for constructing the long jump distance.
Performance of a long jumping is depended on not only
a fast horizontal velocity at the end of the run-up but also
take-off technique and landing in the sand pitch. These
phases of the long jump are very importance and effect to
the distance of the long jump, especially, the run-up phase
which is the first phase of the long jump. Hey (1985) states
that in order to make a fast run up, most long jumps
athletes use 16-24 running strides (around 35-55 m.) The
athlete reached about 95-99 per cent of their maximum
sprinting speed at the end of the run-up [4]. Besides, many
long jump athletes use a check mark at 4-6 strides before
taking-off on the wood board. Therefore, their coach can
easily monitor the accumulated error and also solve run-up
in the first phase of the long jump. The take-off phase is
also one of important phase for the long jump performance.
The long jump athletes have to use a suitable take-off
technique. For instance, the take-off leg angle at about
60-65 degree to the horizontal [5], [6]. While, the flight
and landing phases are a difficult action on the air.
Because, after the take-off the athlete is in free flight, so
the all angular momentum have to be considered, for
example, the forward angular momentum (circling the
arms and legs forward). Finally, the end of the flight phase
is the landing on the sand pitch. Long jumpers have to
prepare their bodies for the landing by lifting their legs up
Journal of Advances in Information Technology Vol. 6, No. 4, November 2015
and extending them in front of the body [4].
According to the long jump techniques, the existing
coaching methods of long jump are monitoring during the
jump, and then suggesting the right actions to the long
jumpers or as a slow motion video which is a popular
method for analysing motion. Nonetheless, they have to
rely on many professional coaches for suggestion
techniques and also to correct the wrong actions of the
long jump. For example, the optimisation performance of
the long jump such as the approach run [4],[5], the take-off
[5], [6],[7],[8],[9], the flight and landing [10], [4], [11].
These research have been studied in the sports science
research in order to improve the long jump techniques for
the athlete’s long jump. Therefore, the knowledge of long
jump techniques are held in professional coaches and long
jumpers. Thus, problems in transferring the skilled
knowledge from professional coaches to the new
generation long jumpers are to be considered. In order to
make robust suggestion techniques. Computer science
technology is an alternative way for helping coaches and
athletes to practice long jump techniques correctly.
Nowadays, the development of digital world with the
wide spaced digital equipment enable the use of
technology to produce huge amount of image and merges
the technology into many automatic applications. In the
computer science research, computer vision techniques
have been employed computer vision techniques for
analysing the human movement, called the action
recognition. The action recognition aims to automatically
analyse and interpret continuity of human movements that
made by a human agent during doing of a task into actions.
The typical action recognition system consists of the
feature extraction, the action learning, the action
classification, and the action segmentation [12]. For
instance, in Thomas (2001) the long jump technique in the
transition from the approach run to the take-off using
Time-Continuous kinematic data has been analysed. They
classified the complex movement patterns by using the
single linkage algorithm. The single linkage algorithm is
based on minimising the distance between two objects
[13]. Besides, Niebles (2010) presents the framework for
recognition complex human activities in the video by
using temporal model [14]. Hsu and his colleaques (2006)
studied an automatically detect the motion during a
standing long jump. First of all the silhouette of the jumper
from video sequence were segmented from the
background for all frames. Then a GA-based search was
used to find the stick model. The stick model points out the
essential joints of the person [15]. In addition, Costas et al.
(2006), they studied an automatic human detection,
tracking and action recognition based on real and dynamic
environments of athlete finding. These sports such as pole
vault, high jump, long jump, and triple jump were tested in
this study [16]. While, in Robin et al. (2012), the use of
image processing for measuring swimming biomechanics
is demonstrated. They analysed techniques to enhance
images for improving clarity and automating detection of
both arms and legs segments [17]. These research used
© 2015 J. Adv. Inf. Technol.
computer vision techniques in order to help for analysing
human movement and also improving athlete skills.
The review of literatures above has shown feasibility to
apply action recognition techniques to long jump
biomechanics training jumpers. With respect to construct
the high performance smart trainer of the long jump. In
this paper of losing knowledge skills, we introduce a new
framework of Knowledge-Based System for long jump
athlete by using computer vision techniques. The goal of
this work is to support the coaching of the long jump
athletes and assist for improving their long jump
techniques. Also, the system is able to transfer the long
jump knowledge to the new generation athletes.
The major contributions of this article are the following:
(i) we propose a novel framework of knowledge-based
trainer for improving skills of the long jump athletes; (ii)
we present a system design of the application of the
computer vision techniques for analysing the
biomechanics of the long jump. (iii) we suggest the
potential of the integrating system design for
recommendation athletes in order to prove their skills with
knowledge management systems.
The organization of this paper are the background and
relate works presented in Section 2. In the flowing, the
conceptual frame work of the knowledge-based trainer for
long jump athletes is described first in the Section 3. In the
Section 4 Knowledge-Based system is designed. Section 5
the preliminary experiment and results are showed at the
end of the paper. Finally, Section 6 the conclusion as well
as the future work are determined.
II. BACKGROUND AND RELATED WORK
A. Long Jump Biomechanics
Long jump principle has been changed over time to the
modern athletics in the mid-nineteenth century. It consists
of sprinting on the runway, taking-off on the wooden
board, flighting through the air and then landing in a sand
pit. An excellence long jumper must be a fast sprinter,
strong legs, a good complex take-off, flight and landing.
About 6.5-7.5 m is the best female long jumper distances,
while the best male distances could be reached around
8.0-9.0 m. [3].
Figure 1. Biomechanics of the long jump.
Fig. 1 shows the long jump biomechanics that consists
of the approach run phase, the take-off phase, flight and
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depend on the angular momentum of they take during
take-off as well as the available time on the air before
landing on the sand pitch. Many coaches recommend
the ’hang’ technique for the novice athletes because they
produce a lower angular momentum and also have less
available time on the air. Whereas, the ’hitch kick’
technique is appropriate for the better athlete. Long jump
styles comprise hang style, stride jump and hitch kick are
shown in Fig 2 [3].
4) Flight distance equation
Fig 3 shows the jump distance that can be calculated
by the summation of the take-off distance, the flight
distance, and the landing distance [20].
Equation (1) is the total of jump distance. Wakai
(2005) states that 90 percent of the total jump distance is
the flight distance. Hence, the biomechanical factors of
the long jumpers flight distance are essential in long
jumping. The effects of gravity are much more than
aerodynamic forces during the flight phase of the long
jumping. Therefore, the jumper may be determined as a
projectile in feet [20]. The trajectory of the COM is
defined by the conditions at take-off as well as the flight
distance is given by
landing phase, and the fight distance can be derived from
all phases of the long jump.
1) Approach run phase
This phase is an important aspect of the long jumping
that lead to a good performance by a fast and accurate
run-up. The tasks of the athlete during run-up compose of
the acceleration to near-maximum speed, lower the body
during almost final step and bring to take-off and take-off
foot on the take-off board accurately.
Run-up velocity: Most long jumpers take about 16-24
percent for striding over a distance of about 35-55 m.
Then the athlete reaches around 95-99 percent of their
maximum sprinting speed. Long jumpers avoid run-up all
100 percent of sprinting speed [18].
Run-up accuracy: The long jump run-up consist of two
phases which are the acceleration phase and the
zeroing-in phase. During the last few strides before
take-off on the board, the athletes are adjusting the length
of their strides in order to check how far they are from the
board. About five strides before the board have been used
for high performance long jumpers. In addition to that,
long jumpers are able to adjust the stride with a small loss
of horizontal velocity. Novice athletes tend to have
mistaken their stride adjustment and high accumulated
error later than highly skilled jumpers. A check mark 4-6
strides has been used for many long jumpers before the
board [19].
Run-up to Take-off: Hay and Nohara (1990) states that
skilled long jumper prepared about two to three strides
before take-off with lower their centre of mass (COM). A
low position into take-off is vital to provide a large
vertical range of motion (ROM). Most long jumpers
spend more time for practicing to lower their COM while,
minimizing any reduction in run up velocity [19].
2) Take-off phase
In order to perform an appropriate take-off technique at
the end of run-up and take-off foot well ahead of COM at
touchdown to produce low position at the beginning of
the take-off. Later a body of jumper pivots up and over
the take-off foot, in at the same time the take-off leg
quickly flexes as well as extends. Bridgett and Linthrne,
(2006) states that a projectile event is principally for long
jumping [5]. Additional, the long jumper desires to
maximise the flight distance of the human projectile by
optimumising take-off velocity and take-off angle. In
2000, Seyfarth and his colleagues revealed that in the
long jumping, the optimum take-off technique will run up
as fast as possible [6]. On the other hand, planting the
take-off leg should be approximately 60-65 to the
horizontal [5].
3) Flight and landing phase
The majority of long jumpers employ either ’hang’
position or execute a ’hitch-kick’ movement. The athlete
actions are considered to control the forward rotation. It
is informed by the body at take-off and also to reach an
effective landing position [10]. Linthorne (2008)
describes the athlete is free flight in the air, so the total
angular momentum of athletes must be preserved. The
forward angular momentum is produced by swinging the
arms and legs. The long jumpers select their technique
© 2015 J. Adv. Inf. Technol.
d jump  d takeoff  d landing
(1)
Figure 2. Long jump style: (a) Hang style, (b) Stride jump, and (c)
Hitch kick.
Figure 3. Diagram of the total jump distance.
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components of fitness consist of speed, power, balance,
flexibility, motion, and force. These components are
considerable factors in order to improve their long jump
skill. On the other hand, the long jump distance will be
increased. Each of the fitness components are described
below:
Speed: Athlete sprints 50 metres, an excellent time is
under 7.6 seconds for a male and under 8.1 seconds for a
female. The sprint test is an essential component to
execute the suitable jump at high speed. A good indicator
of speed is ten stride test to monitor athletes’ ability and
construct efficiently accelerate from a standing start
point. Running 6x20 m from a standing start point that
works for the individual measures the time and distance
covered in ten strides.
Power: The standing long jump or vertical long jump
is the measurement of power. The outstanding result is
over 2.5 metres for males, and over 2 metres for females
from standing long jump test, while excellent vertical
jump test, is over 65 cm for males and over 58 cm for
females. Power is also important in the long jump. Hence,
the competitors require dedicating a maximum power in
the rapid time.
Balance: The stork stand test is used for testing that
excellent reading for both males and females is over 50
seconds. Balance is extremely importance in the flight
stage of the long jump. It aims to gaining the longest
distance possibly from takeoff to landing by smoothly
glide and in one ahead direction in the air.
Muscular Strength: Leg Strength Test in which area of
25 metres is the measurement test. It is constructed by
cones in a straight line. The steps start jogging ten metres,
and then proceed to hop on the dominant leg until it
reaches the end of the cone.
Flexibility: Sit and reach test is a measurement that
excellent reading for males would be over 10 cm, and 15
cm for females. Flexibility is vital for helping athletes to
protect their joints, connective tissue and muscle.
Additionally, it is very importance in assisting the
athletes to reach maximum speed, and power levels in the
long jump. Motion: In order to complete an excellent
jump, the approach run is vital to construct the maximum
speed that influences to the jump. Moreover, momentum
is also important especially angular momentum such as
on arm and leg rotation. In addition, take-off and flight
stages of the jump which are parts of momentum, are
very necessary for a high jump as well as carry forward.
The higher jump makes athletes stay longer in the air, the
longer momentum can carry forward. The arms and legs
in flight are also significant factors by swinging forward,
as it generates further momentum.
Force: is importance in the take-off stage of the long
jump. Take-off on the board with the athlete body can be
applied force in order to push off it and increase the
height and length of jump. Moreover, the force can be
absorbed by bending knees, ankles and hips when the
body land [21].
And
d flight
1


2 gh  2 
v 2 sin 2  
1  1  2


2
 
2g
v sin   


where v is the take-off velocity,  is the take-off angle, and
g is the acceleration due to gravity. h is the relative
take-off height, which is given by:
h  htakeoff  hlanding
where htake-off is the take-off height and hlanding is the landing
height, the range of a projectile launched from the ground
level over a horizontal plane can be derived from
d flight 
v 2 sin 2
g
B. Long Jump Analysis
Long jump can be distinguished into two parts,
sprinting and jumping. The first part is the sprinting
which composes of two phases, the driving phases when
the leg is in touching the ground and the recovery phases.
Another part is the jumping movement of the long jump,
which very similar to sprinting and involves the hip knee
and ankle [21].
1) The optimisation performance of physical fitness
The long jump can be separated into four phases: the
approach running, taking-off, flighting and landing. Each
of these requires many fitness components. First of all,
approach running is essential in order to execute a good
jump that requires the speed fitness component. In this
phase it has been studied and found that the most
influential factors of the jump distance is approach
running. The second phase, take-off with power, needs to
be explosive and fast. The third phase, flight phase,
requires the balance due to the good balance affect to
flight actions in order to perform appropriate landing.
The last phase, landing, is very vital for avoiding athletes
from muscle tissue and joints injury. Muscular strength
and flexibility are also essential. They help protecting the
body of athletes to control and change the direction
shows in Fig 4 [21].
Figure 4. Optimization performance of physical fitness in the long
jump.
C.
Action Recognition
In the computer vision, an action recognition is become
a very importance research topic including motion
2) Methods of assessing the components of fitness
Tanya (2010) addresses the methods for improving
skill, as well as the performance of the long jump. The
© 2015 J. Adv. Inf. Technol.
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recognition [22], [23], facial recognition [24], and
movement behavior recognition [25].
Deniel et al. (2011) addresses the action recognition
which is a sequence of human movements that constructed
by a human agent during the doing of a task. The task of
action recognition will be named actions, for instance,
define the action label for describing an action case when
acted by unlike agents under unlike perspective. The
action recognition system will be done by sending
instructions to the actor then using simple action verb
imperative and comparing with the recognized action
names. It comprises the feature extraction, the action
learning, the action classification, and the action
segmentation. First of all, feature extraction is extracting
postures and motions from video to distinguishable
features between human actions. Secondly, action learning
is the learning statistical model after features extraction is
be done. This model is used to classify a new feature
observation. Thirdly, action segmentation is cutting
motion streams into a single action for example, initial
training sequences that use to learn models. Finally, action
classification is a reasonable comparison of dataset with a
different amount of training and testing, for instance;
different subjects such as size, shape, speed, and style is
shown in Fig. 5 [12]. Moreover, Ke et al. (2013)
demonstrates human activity recognition on video data.
There are three aspects for human activity recognition
such as core technology, human activity recognition
system, and applications from low level to high level
representation [26]. On the other hand, the Fig. 6 shows
typology of video-based human activity recognition
system.
Figure 5. Data flow of the long jump action recognition system.
Figure 6. Typology of video-based human activity recognition system.
questions (queries) submitted through a computer.
Generally, expert systems consist of three parts. The first,
is the knowledge-based component which contains the
information of experts and as well as logical rules. The
second part, is an inference engine that is an interpreting of
the submitted problem against the rules and logic with the
knowledge-based. Finally, the last part is an interface that
allows the user to show the problem in a human language
such as English language [28].
D.
Expert System
The Oxford dictionaries (2010) give the meaning of the
expert system as “A piece of software which uses
databases of expert knowledge to offer advice or make
decisions in such areas as medical diagnosis” [27].
Besides, business dictionary (2015) describes that the
knowledge of an expert in a particular subject in an
artificial intelligence based system (expert system) is
converted into a source code. This source code can be
combined with other such source codes (based on the other
experts knowledge) and also used for responding
© 2015 J. Adv. Inf. Technol.
E.
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Related Works
In the literature review, the human activity recognition
Journal of Advances in Information Technology Vol. 6, No. 4, November 2015
in the video with respect to the sports are considered to
study. In this section we present an overview of some
related works as well as refer the readers to [26], [12], [29]
for the more completed survey. There are several
researchers have studied about analysing the movement of
the long jump. They proposed the long jump techniques in
order to improve the performance of long jump athletes.
For instance, approach run techniques, take-off techniques,
flight and landing techniques jump [18], [10], [5], [6], [8],
[30], [7], [9].
Furthermore, in the computer science research, some
researchers employed computer vision techniques for
analysing the human movements. Thomas has analysed
the long jump technique in the transition from the
approach run to the take-off using Time-Continuous
kinematic data, in 2001. They classified the complex
movement patterns by using the single linkage algorithm
with 57 trials (4.45-6.84 m) of time-continuous data. The
single linkage algorithm is based on minimising the
distance between two objects. In the results, it allows to
identify structural changes of movement during a singular
and individual movement styles within the same
movement type, as well as remarkable specifically
concerning the flight phases [13]. On the other hand,
Niebles (2010) presents the framework for recognition
complex human activities in the video by using a temporal
model. 3-D Harris corner detector was used for extracting
the features [14]. In addition, the library for Support
Vector Machines (LIBSVM) of Chang (2001) was
employed. This library was used to classify the activities
with KTH Human action dataset, Weizmann action
database, and Olympic Sports dataset [31]. Besides, Hsu
and his colleagues (2006) studied an automatic motion
detection during a standing long jump. The silhouettes of
the jumper from video sequence was segmented from the
background for all frames. Pose estimation for an
individual silhouette and Hue-Saturation-Value (HSV)
space were also considered to extract the features. Then
the GA-based search was used to find the stick model. The
stick model points out the essential joints of the person. 20
frames of a standing long jump video sequence have been
used for analysing data. The result shows that silhouettes
and computer can be generated stick model of the second
and the third frames [15]. Moreover, Costas et al (2006)
presents an automatic human detection, tracking and
action recognition based on real and dynamic
environments of athlete finding. They used the Temporal
Signal for segmentation the athlete videos. Additional, the
Human Points Detection algorithm has been employed for
the extracting features. Besides, these sports such as pole
vault, high jump, long jump, and triple jump were
classified by using Silhouette analysis algorithm. 39 video
data set (12 pole vault, 9 high jump, 8 triple jumps and 10
long jumps) were used for analysing. The result shows that
the correct classification rates were (100%) for the pole
vault, (88.9 %) for the high jump, (87.5%) for the triple
jump, and (80%) for the long jump. And also, if some
frames the silhouette estimation algorithm ran failed, the
© 2015 J. Adv. Inf. Technol.
187
system will not lose its stability [32]. While, Robin and his
colleagues used image processing for measuring
swimming biomechanics. They analysed techniques to
enhance images for improving clarity and automating
detection of both arms and legs segments. The Normalized
Cuts techniques were used in the segmentation. Then
Global Probability of Boundary was considered for the
extracting feature. Moreover, a freestyle swimming video
footage has been analysed in this study. The results found
that the Normalized Cut algorithm provides reasonable
super pixel maps. And also the boundary detector can
precisely detect the body and draws an almost closed
contour around swimmer [17]. On the other hand, some
researchers employed the expert system in order to
construct the application for keeping knowledge from
experts. Such as in 2011, Rueangsirarak used Motion
Capture Technology to diagnose falling patterns in elderly
people with a Knowledge-Based System in order to
determine serious falling risks for them and recommended
guidelines for medical treatment [33]. Besides, Oliveira
(2014) presents the framework of a knowledge-based
system in order to support the creation of e-learning
materials. It would be easily adapted for an effective
generation of custom-made e-learning courses or
programmes [34]. In addition, Welter et al., (2011)
developed the concept of image-based case retrieval for
radiological education using content-based. It is called
Content-based image retrieval (CBIR). This paradigm
incorporates a novel combination with aspects of
diagnostic learning in radiology: (i) Experiences in a
training environment combined with the radiologists
working context; (ii) The training cases is up-to date cause
they are stored routine by electronic medical records; (iii)
Support adults learning that appropriate for the patient and
problem-oriented [35].
III. THE CONCEPTUAL FRAME WORK OF
KNOWLEDGE-BASED SYSTEM FOR TRAINING LONG JUMP
ATHLETES
The Fig. 7 illustrates the conceptual framework of the
introduced Knowledge-Based System for Training Long
Jump Athletes. The framework aims at capturing the
knowledge of the expert long jump athletes and also the
suggestion in order to correcting the right movement of
novice long jumpers. Besides, it will improve their skills
and performances of athletes. The system is separated into
four sections as (i) Domain Experts, (ii) Action
Recognition, (iii) Expert System, and (iv) Action
Suggestion.
A.
Domain Experts Section
In this section, we firstly prepare for exploiting
information from long jump experts both the primary and
the secondary resources. The primary information is to be
collected from experts such as trainers, coaches, and
skilled long jumpers. Thereby, the capturing knowledge
either of interviews or captures their movements of long
jump athletes. Besides, the secondary information is the
Journal of Advances in Information Technology Vol. 6, No. 4, November 2015
domain experts, the action recognition, knowledge
databases and also the action suggestion. In order to
suggest good practice to athletes, the expert system will be
train for the long jumping [42]. There are 4 sequences of
this section are firstly, constructing the recognised pattern
(training image data set) and also text suggestion from
expert (training text), secondly, generating the rule of each
segmented objects with experts suggestion, thirdly,
comparing the test movement with the trained pattern by
using similarity function, and finally, suggestion text for
each action segmentation.
collected data that have been analysed in empirical
research for example theory of long jump [3], long jump
analysis [36],[21], optimisation of take-off [20], approach
run research [37], [38], long jump professionals articles
[39], [40], textbooks [2], trainer programs [41], etc. Then
analysis both information primary and secondary in order
to precisely suggestion, as well as recommendation the
right movement of the long jump.
B.
Action Recognition Section
This section includes about action recognition
techniques for recognising the long jump. We will employ
the computer vision technique in order to analysis the
athlete image sequence while acting long jump processes.
It is called the action recognition [12]. There are 4 steps of
action recognition. First of all, object segmentation will
use the static camera configuration with a camera for
capturing the phase of the long jumping of taking-off and
flight phase. Secondly, the long jump feature extraction is
to extract characteristics of the segmented objects for
example, shape, silhouette, colours, and motion. Thirdly,
detection and classification algorithm will be used for
recognising the long jump movements. It is based on the
represented features. Finally, if the data is accepted by
experts, it will be used to train the knowledge-based
system in order to make the master pattern of the long
jump.
C.
D.
Action Suggestion System Section
In this section, an action suggestion system is to give
recommendation or suggestion of correct actions for the
long jump athletes. The suggestion is an idea that will be
accepted or rejected. An action is taken after a decision is
required [43]. Some research proposed successfully using
of suggestion systems such as recognition of employees
for improving their jobs [44], [45], [46]. Thus, we will
apply the suggestion system to improve long jump skills.
Our suggestion system will be provided summary results
that show on the monitor. Moreover, training text for
correcting and suggesting the long jump, for example, the
approach run, the take-off, the flight, and the landing
respectively will be shown as well. In addition, the
training programme of the long jump will be provided to
improve their skill and also long jump distance. Including
the nutrition suggestion for the long jump athletes will be
provided as well.
Expert System Section
In the expert system section, we aim to integrate the
Figure 7. Conceptual framework of a knowledge-based system for training long jump athletes.
Capturing device, (2) Preprocessing, (3) Segmentation of
the video to image sequences, (4) Feature extraction of the
captured video, (5) Training of the captured video sets, (6)
Discriminative of the Action correlation, (7)
Configuration training text suggestion to the image
sequences. Finally, keeping to Knowledge Model. The
Testing process will be provided the suggestion of the long
IV. KNOWLEDGE-BASED SYSTEM DESIGN
Fig. 8 shows the system design of Knowledge-Based
System for the long jump. The system will be separated
into two parts which are the Training process and the
Testing process.
The Training process consist of 8 processes: (1)
© 2015 J. Adv. Inf. Technol.
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Journal of Advances in Information Technology Vol. 6, No. 4, November 2015
jump action. There are 7 processes of the testing process.
There are (1) Capturing device, (2) Preprocessing, (3)
Segmentation of video to image sequences, (4) Feature
extraction, (5) Testing video sets, (6) Similarity
measurement, and Finally, Suggestion Actions report. The
similarity between the training image sequences from the
Knowledge Model and testing image sequences will be
compared, after that, the results will be reported on the
monitor and also recommended the appropriate practice of
the Long Jump Biomechanics.
Figure 8. Knowledge-based system design for the long jump.
Biomechanics.
V. PRELIMINARY EXPERIMENT
TABLE I.
A. Methodology
Participant
Expert (E1)
Novice1 (N1)
Novice2 (N2)
Novice3 (N3)
Novice4 (N4)
Novice5 (N5)
Novice6 (N6)
Novice7 (N7)
Novice8 (N8)
Novice9 (N9)
Novice10 (N10)
The purpose of this preliminary experiment was to
collect pre-test data and evaluate factor that significant
affect the long jump performance. There are two sections
including speed performance and long jump performance.
To do this, we collected speed, long jump distance,
take-off angle, and long jump action. We also recoded
action for constructing the action recognition of system.
1) Participants and jumping protocol
Ten male students of Chiang Mai University and a long
jump expert are participate in the study (Table I). They are
novice long jumpers. The participants were informed
about the procedures and the protocol prior to their
participation. All jumps were completed in the long jump
sand pit. The take-off was performed from the synthetic
running track, flat, dry. Besides, the sand landing area was
leveled with the take-off surface. Each participant
performed one time of 50 metres speed running, and also,
three attempts of long jumps at his preferred. They jumped
without any knowledge information of the long jump
© 2015 J. Adv. Inf. Technol.
AGE, HEIGHT, AND WEIGHT OF THE PARTICIPANTS
Age (years)
39
19
21
18
22
19
19
23
20
18
27
Height (m)
1.80
1.60
1.74
1.79
1.75
1.66
1.80
1.73
1.82
1.74
1.68
Weight (kg)
75
55
70
69
64
51
70
68
67
56
58
2) Procedures
Data collection: Data consist of 2 sections including
speed performance and long jump performance. First
section, speed performance were collected by 50 metres
running. While running, time were caught every 10 metres
(1-10, 10-20, 20-30, 30-40, and 40-50). Then, velocity
were calculated by distance/time. The maximum speed
period of each athlete were collected in order to determine
189
Journal of Advances in Information Technology Vol. 6, No. 4, November 2015
the check mark stride.
The other section, the long jump performance were
collected from the participants, such as, start to take-off
distance, time, long jump distance, and take-off angle
were noted in the form. In addition, long jump action were
captured by a digital camera.
Data analysis: Descriptive statistics were used for
calculating the average as well as standard deviation (SD)
of start point to the take-off board, velocity, jump distance,
and take-off angle.
2) The long jump performance
Due to the fact that some participants left from this
study after speed performance experiment, Eight of
participants (one expert and seven novices) cooperated for
the long jump pretest. Table III lists the average values of
the start distance, velocity, jump distance, and take-off
angle.
3) The relationship between velocity and take-off
angle effected the long jump distance
Fig. 10 illustrates the scatter plot of the relationship
between velocity and take-off angle that effect to the long
jump distance of all participants. The result was separated
into three groups by the long jump distance which are
3m-4.5m, 4.6m-5.5m, and 5.5m-6.5m respectively. It
shows the majority of the long jump distance is around
4.6m-5.5m. Among the plots, we noticed the similar
values of velocity and takeoff angle of 2 long jumpers,
where the different long jump distances are significant.
For instance, two long jumpers tried to jump, the first one
was velocity of 6.96 (m/s) with the take-off angle of
21.49 and the other one was at velocity of 6.93 (m/s) with
the take-off angle of 22.28. Both of the long jumpers have
similar values of velocity and the takeoff angle. The result
revealed that the long jump distances these two
participants are considerably different or around 1.18m
(6.20m and 5.02m respectively). Therefore, we need to
know why the long jump distances are quite different.
What alternative factor that influence the long jump
distance are performance.
B. Results
1) Speed performance
Table II shows velocity of all participants with 10m per
period of 50m distance. Besides, the Fig. 9 illustrates that
almost all velocity of the participants increase slightly to
the peak at the 30-40m period, then declines modestly to
the end point. The participant N8 is the maximum velocity
that rises to 11.90 (m/s). However, it drop rapidly to 8.93
(m/s) at the end point. Therefore, this speed test shows that
all of participants should begin running at the point
between 30m and 40m far from the take-off board, except
for the participant N4, because, he has the peak at the
40-50m period. Hence, his start point should be farther
around 40-50m from take-off board.
TABLE II. VELOCITY OF ALL PARTICIPANTS FROM START TO STOP
50M (10M PER PERIOD)
Velocity of Start to end Distance 50m. (m/s)
Participant
Expert (E1)
Novice1 (N1)
1-10
7.46
8.06
10-20
10.10
8.26
20-30
9.43
9.35
30-40
10.75
11.63
40-50
8.70
8.90
Novice2 (N2)
8.70
8.40
9.26
10.75
8.93
9.43
Novice3 (N3)
9.52
8.00
9.35
10.64
Novice4 (N4)
8.00
9.35
8.85
9.09
10.20
8.77
10.31
10.00
8.62
Novice5 (N5)
8.55
9.52
Novice6 (N6)
6.80
8.62
9.80
9.90
Novice7 (N7)
8.47
8.62
10.00
10.75
8.55
8.93
Novice8 (N8)
7.58
9.01
10.31
11.90
Novice9 (N9)
9.71
8.93
9.26
9.80
8.85
Novice10 (N10)
7.19
7.87
9.43
10.42
8.06
Figure 10. The relationship between velocity (m/s) and Take-off angle
effected to long jump distance (m) of all participants
Figure 9. The relation between velocity of all participants and distance
periods of 50m (10m per period)
© 2015 J. Adv. Inf. Technol.
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or trainers thereby both interviewing and also capturing
long jump motions using video capturing device. Besides,
action recognition is to be implemented to analysing data
then collect the knowledge dataset not only training image
dataset for recognising action but also training text for
suggestion actions. In addition, the expert system will be
developed for training long jump athletes. One
challenging extension is the capability of the system to
provide the suggestion, such as approach run, mark points,
long jump techniques, as well as training programmes for
practices long jump performance.
Figure 11. The comparison of long jumpers with similar velocity and
take-off angle (a) The expert long jumper of (E1) and (b) The novice
long jumper (N1) in Table III
ACKNOWLEDGMENT
This study is supported by the Motion-Capture
Laboratory, College of Arts, Media and Technology
(CAMT), Chiang Mai University (CMU), Chiang Mai,
Thailand in collaboration with the Department of
Rehabilitation Medicine, Faculty of Medicine, Chiang
Mai University Chiang Mai, and also, Department of
physical education and recreation Physical, Rajabhat
Chiang Mai University, Chiang Mai, Thailand. Besides,
the author would like to conveys thanks to Student
Development Division of Chiang Mai University as well
as Mr. Suriyo Thirach for their the long jump activity
supports.
Fig. 11 shows the different movement of long jumping
of two long jumpers, while velocity and take-off angle
are the similar. Nevertheless, it would be able to make
different distance depend on the long jump
action/movement in the air.
TABLE III. VELOCITY OF ALL PARTICIPANTS FROM START TO STOP
50M (10M PER PERIOD)
Participan
t
E1
N1
Start distance
(m)
26:80 ±2:86
27:50 ±0:00
Velocity
(m/s)
6:79 ±0:13
6:67 ±0:40
Jump distance
(m)
6:04 ±0:20
5:30 ±0:27
Take-off
angle ()
21:89 ±0:68
21:68 ±3:44
N3
27:05 ±2:68
6:95 ±0:24
4:91 ±0:15
14:34 ±3:57
N4
27:57 ±0:05
6:18 ±0:75
5:38 ±0:36
14:20 ±1:27
N6
29:99 ±0:02
5:65 ±0:28
4:80 ±0:28
16:99 ±1:12
N8
21:93 ±0:98
6:48 ±0:16
4:80 ±0:27
10:88 ±2:22
N9
27:33 ±1:15
6:85 ±0:02
5:37 ±0:15
14:77 ±3:60
N10
29:63 ±1:50
6:40 ±0:29
3:96 ±0:67
11:88 ±2:11
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Teerawat Kamnardsiri is currently pursuing
Ph.D. program in Knowledge Management at
College of Arts, Media and Technology, Chiang
Mai University, Thailand. He received his B.Sc. in
Technical Education (Computer Technology) and
M.Sc. in Technical Education (Multimedia
Technology) at King Mongkut’s University of
Technology North Bangkok, Thailand. He got a
certificate of the Erasmus Mundus, E-tourism
Project (Euro Asian project 2011-2014). His research areas are
Knowledge Representation, Computer Graphics, Image Processing and
Computer Vision.
Worawit Janchai is a lecturer in the Knowledge
Management Department, College of Arts, Media
and Technologies, Chiang Mai University,
Thailand. He holds PhD in Knowledge
Management from Chiang Mai University, Chiang
Mai, Thailand in 2011, Currently, he is an
Assistant Professor in Knowledge Management
and also the Head of Academic Office of College
of Arts, Media and Technology. His research interests are related to
knowledge workers competency development and workplace learning
especially in the learning organization context.
Pattaraporn Khuwuthyakorn is a lecturer in the
College of Arts, Media and Technologies, Chiang
Mai University, Thailand. She received the B.E
(Electrical Engineering) from Chiang Mai
University (CMU), the M.E.M. and the M.S.E.E.
from Queensland University of Technology (QUT)
and the Ph.D. in Information Engineering from the
Australian National Univesity (ANU). Her
research interests include computational method
development in computer vision, pattern recognition, machine leaning,
hyperspectral image classification and their applications.
Journal of Advances in Information Technology Vol. 6, No. 4, November 2015
Administrative board committee, Royal College of Thai Physiatrists,
Department of Rehabilitation Medicine, Faculty of Medicine, Chiang
Mai University. His research interests include Rehabilitation medicine,
Music therapy, Musculoskeletal medicine, Sports science and medicine,
Electrodiagnosis, Prosthetics & Orthotics.
Parinya Suwansrikham is lecturer of Software
Engineering at College of Arts, Media and
Technology, Chiang Mai University, Thailand.
He received his B.Eng. in Electrical Engineering
from Chiang Mai University and M.Eng in
Computer Engineering from King Mongkuts
University of Technology Thonburi. His research
areas are image processing, computer vision and
information retrieval.
Jakkrit Klaphajone is a lecturer in the
Department of Rehabilitation Medicine, Faculty of
Medicine, Chiang Mai University, Chiang Mai
50200, Thailand. He received the M.D. from
Faculty of Medicine, Chiang Mai University, 1993,
the certificate of Proficiency in PM & R from Thai
Medical Council, 1996 and the fellowship in
Sports medicine and science, University of
Aberdeen, UK, 2000. He is currently positioning at
© 2015 J. Adv. Inf. Technol.
193
Permsak Suriyachan is currently leader of
department of physical education and recreation
Physical, Rajabhat Chiang Mai University, Chiang
Mai, Thailand. He received his B. Ed. (Physical
Education) and M. Ed. (Physical Education) at
Kasetsart University, Thailand. He has been a
guest speaker of the International Association of
Athletics Federations (IAAF). He is an expert at
physical fitness of Sport Authority of Thailand.
His research areas are Physical Education, physical fitness, Athlete
performance practices.