Boletín Técnico, Vol.55, Issue 1, 2017, pp.47-56
Continuous Estimation of Wrist Angles for Proportional Control
Based on Surface Electromyography
1
Weixing Wang1, 2 *
College of Computer Science and Technology, Zhejiang University, Hangzhou 310013, Zhejiang, China
2
College of Mechanical Engineering, Guizhou University, Guiyang 550025, Guizhou, China
*Corresponding author(E-mail: [email protected])
Shouqian Sun
College of Computer Science and Technology, Zhejiang University, Hangzhou 310013, Zhejiang, China
Abstract
Despite the success of pattern recognition in many joints, the possibility of using classification algorithms to
characterize the relationship between wrist muscle activities and angles of motion has rarely been investigated.
Therefore, information about wrist dynamic motion and the effects of wrist motion direction on wrist angle
recognition is lacking. To apply surface electromyography (sEMG) signals in wrist proportional control
applications, the present study aimed to identify continuous joint angles during dynamic motion. We discuss
four directions of wrist motion, including wrist flexion, wrist extension, supination and pronation. To improve
the recognition accuracy, wrist motion was examined under normal conditions and two types of strength
conditions. Using a pattern recognition algorithm, we examined three different classifiers for wrist angle
prediction from the sEMG signal. Then, we assessed the quality of angle regression by analyzing the root mean
squared error and C0 complexity. Our study demonstrates the feasibility of using sEMG as an interfacing device
for proportional control of the wrist.
Key words: Surface Electromyography, Pattern Recognition, Proportional Control, Strength Condition, Motion
Direction.
1. INTRODUCTION
Many studies of upper limb myoelectric device control employ surface electromyography (sEMG) because
it is convenient and noninvasive. sEMG provides rich information from which a user’s intention in the form of a
muscular contraction can be detected using surface electrodes. This information can be used in the control of
human-computer interactions, such as control of exoskeletons and prostheses. The quality of information
extraction to recognize a contraction pattern in myoelectric activity relies on many factors. Classification and
regression techniques and physiological models have been extensively considered by the research community
(Englehart and Hudgins, 2003). Pattern classification techniques can be used to identify a variety of intended
arm movements based on distinguishing characteristics of sEMG patterns. Kuiken et al. (2009) demonstrated
that a pattern recognition algorithm can be used to decode sEMG data from muscles and to provide intuitive
control of powered elbows, wrists, and hands. For the wrist, most pattern recognition approaches have focused
mainly on static muscle contractions to separate different movement classes from each other. For instance,
Ajiboye and Weir (2005) used 3 electrodes to collect sEMG data from the forearm, with classification
accuracies for 3 wrist classes (wrist flexion/extension and either wrist pronation or supination) ranging from
74% to 99%. However, more natural interactions with devices require proportional control of dynamic motion,
and the recognition of movement class is more suitable for traditional threshold control. Ideally, devices would
follow body movement, which requires control that enables intuitive replication of human body
functions(Oskoei, 2007).
The joint angle has also been estimated based on sEMG signals by applying the proportional myoelectric
control method(Tang et al., 2014). Christopher et al. (2011) performed a feasibility study that demonstrated that
an appropriately designed artificial neural network was capable of predicting continuous and simultaneous
elbow and forearm joint angles. Koo and Mack (2005) predicted elbow joint angles using EMG signals and
reported large errors ranging from 9.5° ± 3.5° to 34.64° ± 7.79°. Aung and Jumaily (2012) used sEMG to
estimate the shoulder angle and control a virtual human model based on back propagation neural networks
(BPNNs). These studies provided data on arm muscles based on time and frequency domain measurements.
However, there have been no analyses of wrist movement influenced by forearm muscles; thus, information
about wrist dynamic motion and the effects of wrist motion direction on wrist angle recognition is lacking.
Enhancing the recognition accuracy of wrist angles would enable more natural, coordinated movements and to
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improve assistant devices. The wrist angle varies over time during dynamic motion, and sEMG is influenced by
many factors. Accurate estimation of continuous variables is a key problem for motion control of assistant
devices. First, there are multiple directions of wrist movement, including wrist flexion, wrist extension, and
supination and pronation of the wrist joint. Supination and pronation are coordinated movements of the wrist
and forearm. In addition, assistant devices can produce functional resistance of motion itself, which influences
accuracy. Although sEMG has been examined in a number of laboratory strength studies, it has typically been
used as a method for monitoring muscular fatigue rather than to quantify muscle activity itself. Masakado et al.
(1996) studied the relative importance of motor unit recruitment in changing static or isometric muscle strength
levels, which were shown to depend on the mode of contraction, such as isotonic (i.e., sustained constant
strength) compared to anisotonic (i.e., strength-varying). Loram et al. (2005) examined postural control at the
human wrist in extension to examine the control of loads with negative and positive effects and the extent to
which postural control relies on the conscious perception of load. These studies indicate that strength properties
influence the nature of the command required to control motion.
To apply sEMG signals in wrist proportional control applications, the present study aimed to identify
continuous joint angles in four directions of wrist motion. To improve recognition accuracy, this study discusses
wrist motion under normal condition and two types of strength conditions, including constant torsion strength
and grip strength. Using a pattern recognition algorithm, we examined the feasibility of applying BPNNs, a
support vector machine (SVM) and a polynomial classifier (PC) for wrist angle prediction from the sEMG
signal. Then, we assessed the quality of angle regression by analyzing the root mean squared error (RMSE) and
C0 complexity. The purpose of this research was to increase the recognition accuracy of wrist angles using
sEMG by comparing the 4 motion directions and 3 strength factors to enable better control of the humancomputer interaction.
2. METHOD
2.1. Signal Preprocessing
Basmajian and Deluca (1985) indicated that the pronator teres (PT) and pronator quadratus (PQ) are the
prime pronating muscles. Wrist flexion is generated by the muscles of the flexor carpi radialis and flexor carpi
ulnaris (FCU), and extension is generated by the extensor carpi radialis (ECR) and extensor carpi ulnaris. In our
study, the four selected muscles were the FCU, ECR, PT and PQ. Before electrode positioning, the skin beneath
the electrodes was cleaned with alcohol. Although no electrode position isolated a single muscle, placing an
electrode on the skin above a specific superficial muscle should ensure that the largest contribution to the signal
at that location originates from the desired muscle. The muscles and the locations of each muscle’s electrodes
were selected to eliminate negative effects of muscle overlap.
The sEMG signals were processed off-line in Matlab software (The MathWorks, version 8.3.0.532, 64-bit,
2014). We used a high-pass filter with a cut-off frequency of 1 Hz to remove the bias and rectify the signal. A
low-pass filter was used to remove the highest frequencies. The sEMG signal is a nonlinear signal; thus, the
sample signal should be divided into a short time window to retain the stability of the signal. The segment
length should be sufficiently large to avoid degradation of the classification performance because the bias and
variance of feature estimators increase as segment length decreases. However, the segment length should also be
sufficiently small to satisfy the myoelectric control application. The myoelectric control must supply the control
commands in less than 300 ms; thus, the sEMG signals were partitioned into 250-ms windows in this work.
2.2. Feature Extraction
Feature extraction is the dimensional reduction of the raw sEMG input to form a feature vector. The
accuracy of the pattern classification system almost entirely depends on the choice of these features (Hudgins et
al., 1993). Many studies have attempted to explore and examine the appropriate feature vector for sEMG signal
classification applications. Feature extraction methods vary and mainly include time domain, frequency domain
and time-frequency domain analysis methods. Compared to frequency domain and time-frequency domain
features, time domain features have been widely used in myoelectric control because of their signal
classification performance in low-noise environments and their lower computational complexity (Phinyomark et
al., 2012). Time domain features are usually used to detect muscle contraction, muscle activity and onset
detection. Therefore, the time domain characteristic, which reflects the contribution of each muscle group to the
process of motion, is often used in human-computer interaction. This study selected feature extraction, which is
convenient to calculate and easy to control in real time. Root mean square (RMS) features are proposed in this
study. RMS is a relatively common sEMG signal time-domain feature. It reflects the characteristics of the
amplitude change in sEMG in the time dimension and has good real-time performance. RMS is defined as the
square root of the mean over time of the square of the vertical distance of the graph from the resting state, which
can be expressed as in (1).
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RMS 
1
N
N
x
2
(1)
k
k 1
where N is the number of data samples in the time window, and Xk is the k-th data sample in the time
window. To eliminate the impact of individual factors, normalization was performed for RMS. The obtained
features were normalized to a value between 0 and 1.
2.3. Classification methods
In previous pattern classification research, various linear and nonlinear classifiers have been proposed for
myoelectric control purposes in the literature (Ison and Panagiotis, 2014). In this work, we proposed using
BPNN, SVM and PC wrist motion classifications and then compared the accuracy of their classifications.
The artificial neural network is the most popular alternative method used to map nonlinear relationships.
Sepulveda et al. (1993) first used a feed-forward neural network model with a back-propagation algorithm in a
supervised manner to map transformations between sEMG and joint angles. BPNNs can approximate any
nonlinear mapping relationship and are global approximation methods; thus, BPNNs have good generalization
ability and good fault tolerance. In this study, a three-layer BPNN was constructed with sEMG signals as the
input to predict the angle of the wrist. The input layer consisted of four nodes, which represent four sEMG
signals. The data were trained with a Levenberg-Marquardt algorithm in the hidden layer, in which the nodes
were set to 20. The output layer consisted of only one node, which predicted the angle of the wrist. The target
was the motion angle of the wrist.
The SVM theory introduced by Oskoei and Hu (2008) exhibits good performance in classification and
regression problems and separates data that cannot be separated linearly. The SVM maps data onto a highdimensional space using a kernel function. In this study, the radius basis function(RBF) was applied to build
recognition models. The mapped data were then expected to be easily classifiable by a hyperplane in the highdimensional space. In the training phase, the training dataset was cross-validated to build the motion pattern
recognition model.
The PC, which has been used in previous studies, provides a statistical estimate of the correlation between
sEMG and angles. The effectiveness of the PC for sEMG pattern recognition was also demonstrated by (Yousef,
2006). The PC renders the results intrinsically more stable and rapid in implementation of the control system.
The training process of the PC includes two steps. The first step is polynomial expansion. The second step is
model training with the least-square criterion. The polynomial order is the only hyperparameter that must be
decided. We selected 2 as a favorable choice for sEMG pattern recognition. The angles of wrist motion were the
target variable. The RMS feature vectors of four channels were the independent variables.
The experiment uses a 3-fold cross validation method. The collected samples were randomly divided into
three groups, two groups were selected as training set and one group was used as the test set. The recognition
rate was the average of 3 results. We normalize the training set and the test set, and then send to the network.
2.4. Data Analysis
The quality of regression was assessed using the RMSE. The RMSE represents the predictive value of the
discrete degree and is also called the standard error. The best fit is obtained when RMSE = 0.
RMSE 
1
N
N
 ( y i - y i, )
2
(2)
k 1
where N is the number of (samples, targets) data points, Yi is an i-th actual value, and Y’i is an ith predicted
value. The averaged classification error for each subject was assessed off-line by fourfold cross validation of the
training dataset. Classification errors were calculated by dividing the number of erroneous class decisions by the
total number of decisions made.
Complexity, such as the Lyapunov exponent, correlation dimension, C0 complexity, Kolmogorov
complexity and Lempel-Ziv complexity, is used to analyze physiological signals. Electromyography complexity
can characterize the orderliness and randomness of sEMG and thus has often been used to study time-series
problems of electromyography. Although the methods of complexity assessment cannot distinguish among the
individual contributions of motor unit synchrony, common myoelectricity input and electrode crosstalk, the
referenced analysis of the effect of muscles on the correlation of the sEMG signal might offer useful insight into
the potential for crosstalk. The crosstalk in forearm muscles and the signal contamination are very large due to
the proximity of relatively small muscles (Kong et al., 2010). The synergy of these muscles will greatly affect
the accuracy of recognition. By comparing the averaged complexity, we can evaluate the influence of different
torsion and grip strength in four directions of wrist motion. In this study, we chose the improved C0 complexity.
A detailed description of the algorithm was reported in (Cai and Sun, 2011). The method can obtain results for
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short-range data robustly and does not require excessive coarse graining processing of continuous time
sequences. In our study, the pre-processed signals were partitioned into 250-ms windows.
x(n) performs the discrete Fourier transform to F(j):
(3)
F ( j )  F [ x ( n )]
where x(n) is the sEMG signal in each channel and n is the length of the sequence. The mean square value
of F (j) is given by the following:
1 n
2
(4)
aveF   F ( j )
n j 1
Greater than average frequency components are considered regular parts of the contribution. Less than or
equal to average frequency components are random parts of the contribution. The algorithm retains only the
regular parts of the contribution.
 F ( j ), F ( j ) 2  aveF
(5)
F ( j)  
2
 0, F ( j )  aveF
The C0 complexity measure is computed by the following:
n
C0 

j 1
F ( j )  F ( j)'
2
 n

  F ( j) 
 j 1

2
(6)
Analysis of variance (ANOVA) was used to assess the significance of differences in the sEMG responses
of the four motions in each muscle. Two-factor ANOVA for repeated measures was used to investigate the
performance of motion directions and strength. Directions and strength were independent variables with four
levels and three levels, and RMSE was the dependent variable. Significance was accepted at P < 0.05.
2.5. Experiment Protocol
The five male subjects in the study were normal volunteers (mean ± standard deviation [SD]; age = 23.5 ±
4.2 years, height = 168.3 ± 8.1 cm, weight = 61.2 ± 15.4 kg) without a known history of any neuromuscular
disorder. All subjects were right handed and provided written consent for participation in this study after being
fully informed of the purpose of the investigation and the experimental protocols. All experiments were
performed in accordance with the Declaration of Helsinki.
The experimental equipment (Figure 1.) was set to a constant torsion strength arbitrarily, according to the
requirements of the experiment, to maintain isotonic contraction for the forearm muscles in wrist rotation and
extension/flexion. The equipment had a handle that could rotate freely while fixed on the axis of rotation or
stretch through a wire connection. The rotation angles of the wrist were measured with a rotary potentiometer
(RV30YN30S, TOCOS, Japan) integrated into the equipment. The potentiometer had a voltage range of +/- 2 V,
which corresponds to a complete 360° range of motion. Wrist extension/flexion angles were measured with a
Bend Sensor-T9550 (FlexComp Infiniti System, Thought Technology Ltd., Canada) fixed on the subject's wrist.
The bend sensor can measure 180° of joint motion. In addition, the Force & Load Sensor (B201 Tekscan’s ELF
TM Data Acquisition Systems, Tekscan, Inc., USA) was installed on the handle. The pressure value was
measured to ensure that the wrist movement was at constant grip strength. The chair could be adjusted for height,
allowing the subject’s body and arm to maintain a comfortable angle. The equipment had an elbow rest to allow
the upper arm to remain stable in wrist motion. The sEMG signals during experiments were collected and
amplified by a ten-channel digital sEMG system (FlexComp Infiniti System, Thought Technology Ltd., Canada).
The sensors (MyoScan sensor), which were connected with electrodes, recorded sEMG signals at up to 1,600
micro-volts (µV) over an active range of 20 to 500 Hz. In the experiment, all data were collected at 1,024 Hz.
The raw sEMG signals and other sensor signals were transmitted to a personal computer through a serial
interface (USB).
The day before the experiment, the maximum strength level of the right wrist of each subject was measured
in torsion and grip. For the torsion strength factor, although the main effect of direction indicated that the
supination torque was stronger than the pronation torque(O’Sullivan and Gallwey, 2002), to ensure the unity of
the experiment for each subject, we chose the averaged value as the maximum strength. Movement in each
direction was performed 3 times at 5 min intervals. In preliminary experiments, the largest wrist torque used (5
N.m) was equivalent to 40 ± 0.75% of the maximum. Subjects had little difficulty maintaining the target wrist
angle for the duration of a trial; thus, we believed that approximately 20% of the maximum strength was
produced relatively easily. Typically, a person’s wrist grip strength is greater than the torsion strength. To
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compare the influence of the recognition under the same strength level conditions, we chose a 20% maximum
grip strength adjusted according to the average value of each subject in the experiment.
Figure 1. The experimental equipment
During the experiment, the subjects were seated in the chair while looking forward and naturally
maintained the body vertically. There were four directions throughout the experiment, including wrist extension,
wrist flexion, supination and pronation. In the experiments with the four wrist motion directions, the subjects
performed normal motion, motion with 20% torsion strength and motion with 20% grip strength in contractions
with a duration of 5 seconds. In the normal motion, each subject held the handle and performed wrist motion
starting from the neutral position to the joint position of the maximum angle. In each direction of the wrist, the
neutral position was the vertical position of the handle. The subjects were instructed to keep all other joints
stationary and, in particular, to avoid moving the body and elbow during the test. Each subject was asked to
perform the motions at the same speed. At the beginning of an experimental session, the subjects familiarized
themselves with the protocol by performing the mirrored motions with instructions from the experimenter. In
the experiments, the subjects were provided with consistent, standardized verbal encouragement from the
experimenter. The subjects were prompted to perform the motions at (Table 1). A minimum of 1 min of rest was
allowed between each motion to mitigate the accumulation of fatigue.
Table 1. Protocol Information
Number
Action
1
Wrist supination (WS)
2
Wrist pronation (WP)
3
Wrist supination + 20% maximum torsion strength (WST)
4
Wrist pronation + 20% maximum torsion strength (WPT)
5
Wrist supination + 20% maximum grip strength (WSG)
6
Wrist pronation + 20% maximum grip strength (WPG)
7
Wrist flexion (WF)
8
Wrist extension (WE)
9
Wrist flexion + 20% maximum torsion strength (WFT)
10
Wrist extension + 20% maximum torsion strength (WET)
11
Wrist flexion + 20% maximum grip strength (WFG)
12
Wrist extension + 20% maximum grip strength (WEG)
Repetition
5
5
5
5
5
5
5
5
5
5
5
5
3. RESULTS AND DISCUSSION
SEMG signals from the four muscles were successfully measured and used to control the wrist. As shown
in Figure 2., the estimation errors of WSG and WPG were very large when the BPNN classifier was used, with
averaged RMSE values, respectively, of 20.9937 and 19.3699. The RMSE value had greater volatility in wrist
rotation. In wrist extension/flexion, the recognition accuracy steadily increased significantly. The maximum
value (RMSE = 11.8095) was obtained in WFG, and the minimum value (RMSE = 7.3159) was obtained in
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WET. ANOVA indicated that both motion direction and strength had significant effects on the recognition of
wrist angle (P < 0.05). In the different directions, the recognition accuracy was greater for wrist pronation than
for wrist supination or wrist extension superior wrist flexion. The results demonstrated that the strength
conditions of the wrist influenced the recognition accuracy more than the motion directions. At constant torsion
strength, the RMSE values were lower than during normal motion, resulting in improved recognition accuracy.
For the identification of wrist angles, constant grip strength affected the stability of the joint. The recognition
accuracy was lower in the grip strength condition than in the normal condition, and the relationship between
sEMG and the joint angles was much more complicated under the grip strength condition. The instability also
produced inconsistent results.
Figure 2. Boxplot of RMSE values using the BPNN in all experimental conditions
The curves of the actual angle and the predicted angle using SVM classification are shown in Figure 3. In
the figure, the light colors represent small RMSE values, and dark colors represent large values. The curve
fitting was very good in WET motion, with the smallest prediction error. In WP and WST motions, there were a
large offset, which was closely related to the RMSE value. The angle estimation of the rotation motion was not
very accurate compared to the extension/flexion motion. In addition, the two curves had relatively large offsets
at each motion of the starting point and inflection point, which might indicate greater crosstalk between the
muscles in the turning point of the motion.
Figure 3. The curve of the actual angle and the predicted angle using BPNN
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Figure 4. depicts the prediction error of the BPNN compared to SVM and PC for wrist motions. The
recognition accuracy was highest for BPNN and lowest for PC. In addition, the patterns of change of the RMSE
value were similar for the three types of classification algorithms for all motion. Figure 5. demonstrates the C0
complexity of a subject measured during twelve types of wrist motion, and the curve shows the averaged value.
The complexity trends of all subjects were similar. The experiments indicated that the C0 complexity was
sensitive to the applied strength but not the direction of motion. The minimum averaged C0 value (0.03907) was
observed for WET motion. The C0 value under different strength conditions indicated different levels of muscle
activity. The complexity of the forearm muscles was higher in the normal condition than the motion under
torsion strength. Under constant grip strength, the degree of randomness of sEMG increased, and the C0 value
exhibited instability that was often greater than the wrist motion under normal conditions.
Figure 4. Comparison of classification algorithms
Figure 5. The averaged C0 complexity of each motion
This study demonstrated the feasibility of using sEMG as an interfacing device for proportional control of
the wrist. Accuracy is an important task factor because it influences the amount of control and the type of
motion required to complete a task. Therefore, the factors affecting the accuracy of recognition must be
discussed in wrist dynamic contraction.
The experiment revealed that different directions led to different recognition accuracies of wrist angles. In
different directions, the recognition accuracy rate of extension/flexion motion was significantly higher than that
with pronation/supination. Ljung et al. (1999) reported that muscle lengths changed with wrist flexion and
extension, with similar but smaller changes resulting from forearm rotation and muscle length changes, which
alter each muscle’s moment potential at a given joint, further complicating the sEMG-angle relationship.
Compared to pronation/supination directions, wrist extension/flexion rendered motion control more automatic,
and less resource synchronization was required. These findings might explain why the recognition accuracy of
wrist flexion/extension was superior to that of wrist rotation. Furthermore, compared to the other two directions,
the results of recognition were higher in flexion and pronation. These results are consistent with major roles of
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some muscles, such as extensor activity increasing with wrist extension and pronation(Di and Keir, 2010). The
total moment-generating capacity of the extensor muscles crossing the wrist was less than that of the flexors due
to a smaller total physiological cross-sectional area and smaller moment arms. Co-contraction of the extensors,
as observed in the present study, is likely a control strategy to increase joint stiffness and minimize deviation
from the desired position. Consequently, the extensor muscles required a greater proportion of maximal
activation to generate the forces required to stabilize the wrist. Some studies have focused on discomfort
adaptations, which could provide some evidence to explain the level of recognition accuracy. O’Sullivan and
Gallwey (2005) reported that discomfort was considerably higher for pronation torque than for supination torque.
Therefore, in wrist rotation, the level of recognition accuracy might be explained by comfort or discomfort.
Similarly, there was sufficient evidence to suggest that the wrist joint extensors are more facilitated than the
flexors. This tendency is supported by the behavioral differences observed by Vallence et al. (2012), who
reported greater precision (lower coefficient of variation) for wrist extension than wrist flexion. Thus, we
estimated that wrist extensors are used relatively more frequently than flexors in everyday work, possibly
leading to greater adaptation of wrist extensors to strength maintenance tasks.
The RMSE values suggested greater stable dynamic activity of the wrist sEMG and greater accuracy of
recognition in the appropriate torsion strength condition. The dynamic motion of the wrist changed the geometry
of the forearm muscles, which may also lead to common crosstalk from muscles adjacent to the target muscle.
Mesin et al. (2009) demonstrated that crosstalk is particularly relevant when the signal intensity produced by the
target muscles is relatively low. Therefore, in the course of the motion with a certain degree of constant torsion
strength, target muscles will strengthen the power output. The number of muscle fibers recruited and the
discharge volume increased, enabling reduced crosstalk and a higher recognition rate compared to the normal
state of motion. In the motion of constant grip strength, the number of forearm muscles that played major roles
were increased, leading to a change in the direction of the strength application. Some studies have suggested that
extensor activation for maintaining optimal wrist position is important to achieve effective grip strength(Mogk
and Keir, 2003). Grip strength was produced by the complex activation of the forearm muscles. Grip strength
requires muscle co-activation in forearm movement to ensure wrist stability, resulting in an increase in common
crosstalk from the adjacent muscles. Under grip strength conditions, the RMSE values of wrist rotation (WSG
and WPG) were significantly higher than those of wrist extension/flexion (WFG and WEG). Handgrip strength
is produced by the complex co-activation of forearm muscles, including both flexors and extensors. The large
number of muscle contractions required with the combination of grip strength and wrist rotation may have
resulted in increased error and, consequently, the relatively high value of C0 complexity in the motion of
constant grip strength. In normal motion, some error might have come from the subjects’ lack of ability to match
the requested contractions in balanced proportions, particularly given the absence of proprioceptive feedback. In
addition, opposing muscles might be recruited in some contractions.
We compared the performance of three different classifiers: BPNN, SVM and PC. The BPNN exhibited the
highest performance among the three classifiers but also had the highest computational cost. The classification
method must be further improved to satisfy the time requirements of a real-time system. The results for the three
classifiers also demonstrated that the RMSE values of wrist angle recognition were the most unstable in the
initial stage and at the end. At different stages of contraction, sEMG had some undetermined states; therefore,
classification errors generally occurred at the junction of the motions. In addition, due to the inertia generated by
the equipment, the muscle cannot respond quickly to the excessive state. Some researchers, such as Phinyomark
et al. (2014), have consequently proposed detecting and eliminating the data segment in the transition state to
improve the accuracy of control. This improved method of classification is beyond the scope of our research.
C0 complexity completely reflected the differences in the number of motor units involved in muscle
activity and the discharge frequency of the motor unit. Our experiments demonstrated that increasing the torsion
strength to the wrist movement appropriately reduced the complexity. Our view is that the synchronized actions
of numerous motor units result in a synergistic reaction under certain strengths. The complexity of the forearm
muscle is higher at low strength levels than at high strength levels and decreases with increasing strength. We
attributed this increase to an increase in the number of motor units and strengthened activities of
synchronization; however, in the experiment, adding the appropriate grip strength to the wrist motion increased
the complexity of the movement. We believe that other muscles control the grip strength level in addition to the
muscles controlling motion directions. The muscles generating grip strength and the muscles controlling the
direction of motion were not identical, in contrast to the torsion strength condition. The increased C0 complexity
also demonstrated that the random components of the signal increased, whereas the stability of the operation
decreased.
4. CONCLUSIONS
Despite the success of using pattern recognition in other joints, the use of classification algorithms to
characterize the relationship between wrist muscle activities and the angle of motion has rarely been
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investigated. In this study, we demonstrated that the wrist angle can be accurately estimated from sEMG signals
during dynamic contractions using classification algorithms. A number of factors might affect the prediction
performance. Our study is one of the most comprehensive examinations of the relationships among motion
directions, torsion strength and grip strength. The results also revealed that the estimation performance of the
BPNN was significantly better than that of the SVM and PS. Analysis of relevant factors revealed that the angle
recognition accuracy of wrist flexion-extension was higher than that of pronation-supination and the condition
of constant torsion strength was higher than the condition of constant grip strength. However, the recognition
accuracy of the device is not sufficient for use in human-computer interactions. Burdea and Coiffet (2003)
summarized the accuracy and latency of commercially available interfacing devices. The accuracy of
commercially available systems ranges from 0.15° to 5°. In future research, we will focus on the integration of
sEMG and other physiological signals to improve the recognition accuracy of the wrist angle.
ACKNOWLEDGEMENTS
This work was supported by the National Natural Science Foundation of China (No. 61402141, 61562072).
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