• Journal of Biomedical Engineering Research
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• v.29 no.6
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• pp.502-511
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• 2008

This study was designed to develop a new algorithm to extract the voluntary EMG and the evoked EMG from a mixed EMG generated when the muscle is stimulated both voluntarily and by electrical stimulation in the FES system. The proposed parallel filter algorithm consists of three phases: (1) Fourier transform of the mixed EMG, (2) multiplication of the transformed signal to two frequency functions, and (3) inverse Fourier transform. Four incomplete spinal cord injured patients participated in the experiments to evaluate the algorithm by measuring the knee extensor torque and the EMG signals from the quadriceps. Two functions of the algorithms were evaluated: (1) extraction of the evoked EMG and (2) the voluntary EMG from the mixed EMG. The results showed that the algorithm enabled us to separate the two EMG components in real time from the mixed EMG. The algorithm can and will be used for estimating the voluntary torque and the evoked torque independently through an artificial neural network based on the two EMG components, and also for generating a trigger signal to control the on/off time of the FES system.

• Physical Therapy Korea
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• v.22 no.4
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• pp.17-26
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• 2015

The purpose of this study was to investigate the effects of visual electromyography (EMG) biofeedback on the EMG activity of the lower trapezius (LT), serratus anterior (SA), and upper trapezius (UT) muscles, the LT/UT and SA/UT EMG activity ratios, and the scapular upward rotation angle during scapular posterior tilting exercise (SPTE). Twenty-four subjects with round-shoulder posture participated in this study. The EMG activities of the LT, SA, and UT were collected during SPTE both without and with visual EMG biofeedback. The scapular upward rotation angle was measured at the baseline, after SPTE without visual EMG biofeedback, and after SPTE with visual EMG biofeedback. The LT, SA, and UT EMG activities, and the LT/UT and SA/UT EMG activity ratios were analyzed by paired t-test. The scapular upward rotation angle was statistically analyzed using one-way repeated analysis of variance. If a significant difference was found, a Bonferroni correction was performed (p=.05/3=.017). The EMG activities of LT and SA significantly increased, and the EMG activity of UT significantly decreased during SPTE with visual EMG biofeedback compared to SPTE without visual EMG biofeedback (p<.05). In addition, the LT/UT and SA/UT EMG activity ratios significantly increased during SPTE with visual EMG biofeedback compared to SPTE without visual EMG biofeedback (p<.05). Significant increases were found in the scapular upward rotation angle after SPTE without and with visual EMG biofeedback compared to baseline (p<.017), and no significant differences were observed in the scapular upward rotation angle between SPTE without and with visual EMG biofeedback. In conclusion, SPTE using visual EMG biofeedback may be an effective method for increasing LT and SA activities while reducing UT activity.

• Journal of Korean Academy of Nursing
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• v.20 no.2
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• pp.195-213
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• 1990

The purpose of this study is to assess if EMG biofeedback training with progressive muscle relaxation training is effective in reducing the EMG level in patients with tension headaches. This study which lasted from 23 October to 30 December 1989, was conducted on 10 females who were diagnosed as patients with tension headaches and selected from among volunteers at C. University in Seoul. The process of the study was as follows : First, before the treatment the baseline was measured for two weeks and the level of EMG was measured five times in five minutes. And then EMG biofeedback training was used to six weeks, 12 sessions in at and progressive muscle relaxation was done at home by audio tape over eight weeks. Each session was composed of a 5-minute baseline, two 5-minute EMG biofeedback training periods and a 5-minute self-control stage. Each stage was followed by a five minute rest period. So each session took a total of 40 minutes. The EMG level was measured by EMG biofeedback (Autogenic-Cyborg : M 130 EMG module). The results were as follows : 1. The average age of the subjects was 44.1 years and the average history of headache was 10.6 years(range 6 months-20 yens). 2. The level of EMG was lowest between the third and the fourth week of the training except in Cases I and IV. 3. The patients began to show a nonconciliatory attitude at the first session of the fifth week of the training.

• Journal of Biomedical Engineering Research
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• v.17 no.3
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• pp.319-326
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• 1996

Clinical myography(EMG) is a technique for diagnosing neuromuscular disorders by analyzing the electrical signal that can be records by needle electrode during a muscular contraction. The EMG signal arises from electrical discharges that accompany the generation of force by groups of muscular fiber, and the analysis of EMG signal provides symptoms that can distinguish disorder of mLecle from disor- ders of nerve. One of the methods for analysis of EMG signal is to separate the individual discharge-the motor unit action potentials(MVAPS) - from EMG signal. But we can only observe the EMG signal that is a superimposed version of time delayed MUAPS. To obtain the information about MUAP(, i.e., position, firing number, magnitude etc), first of all, a method that can separate each MUAP from the EMG signal must be developed Although the methods for MUAP separation have been proposed by many researcherl they have required heavy computational burden. In this paper, we proposed a new method that has less computational burden and performs more reliable separation of superimposed EMG signal using wavelet filter which has multiresolution analysis as major property. As a result, we develope the separation algorithm of superimposed EMG signal which has less computational burden than any other researchers and exacutes exact separation process. The performance of this method has been discussed in the automatic resolving procedure which is neccessary to identify every firing of every motor unit from the EMG pattern.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.211-220
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• 2023

Surface electromyography (sEMG) is a noninvasive method used to capture electrically muscle activity, which can be easily measured even during exercise. The basic unit of muscle activity is the motor unit, and because an sEMG signal is a superposition of motor unit action potentials, analysis of muscle activity using sEMG should ideally be done from the perspective of motor unit activity. However, conventional techniques can only evaluate sEMG signals based on abstract signal features, such as root-mean-square (RMS) and mean-power-frequency (MPF), and cannot detect individual motor unit activities from an sEMG signal. On the other hand, needle EMG can only capture the activity of a few local motor units, making it extremely difficult to grasp the activity of the entire muscle. Therefore, in this study, a method to visualize the activities of motor units in a single-channel sEMG signal by relocating wavelet coefficients obtained by redundant discrete wavelet analysis is proposed. The information obtained through this method resides in between the information obtained through needle EMG and the information obtained through sEMG using conventional techniques.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.5
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• pp.843-850
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• 2017

The EMG amplitude estimator, which has been investigated as an indicator of muscle force, is utilized as the control input to artificial prosthetic limbs. This paper describes an application of the optimal EMG amplitude estimator to the surface EMG signals recorded during constant isometric %MVC (maximum voluntary contraction) for 30 seconds and reports on assessing performance of the amplitude estimator from the application. Surface EMG signals, a total of 198 signals, were recorded from biceps brachii muscle over the range of 20-80%MVC isometric contraction. To examine the estimator performance, a SNR(signal-to-noise ratio) was computed from each amplitude estimate. The results of the study indicate that ARV(average rectified value) and RMS(root mean square) amplitude estimation with forth order whitening filter and 250[ms] moving average window length are optimal and showed the mean SNR improvement of about 50%, 40% and 20% for each 20%MVC, 50%MVC and 80%MVC surface EMG signals, respectively.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.1
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• pp.194-199
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• 2016

EMG has specific information which is related to movements according to the activities of muscles. Therefore, users can intuitively control a prosthesis. For this reason, biosignals are very useful and convenient in this kind of application. Bioimpednace also provides specific information about movements like EMG. In this study, we used both EMG and bioimpedance to classify the typical hand gestures such as hand open, hand close, no motion (rest), supination, and pronation. Nine able-bodied subjects and one amputee were used as experimental data set. The accuracy was $98{\pm}1.9%$ when 2 bio-impedance and 8 EMG channels were used together for normal subjects. The number of EMG channels affected the accuracy, but it was stable when more than 5 channels were used. For the amputee, the accuracy is higher when we use both of them than when using only EMG. Therefore, accurate and stable hand motion estimation is possible by adding bioimepedance which shows structural information and EMG together.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.81-89
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• 2020

The purpose of this study is to investigation the effects of functional compression clothing on muscle function by comparing the iEMG response of muscle during exercise according to the wearing of taping applied functional clothing. Six men in their twenties in Chungcheongnam-do were selected for the study. Resistance exercise was performed by cross-distributing the conditions of wearing and not wearing functional clothing. Resistance exercises for iEMG measurements are biceps curl, wrist curl, reverse wrist curl, kickback and push-up. iEMG measurement muscles were the biceps brachii, triceps brachii, extensor carpi ulnaris, flexor carpi radialis. During biceps curl exercise, the iEMG of triceps brachii, biceps brachii wearing condition was lower than the non-wearing condition. During kickback exercise, the iEMG of triceps brachii, extensor carpi ulnaris wearing condition was lower than the non-wearing condition. During reverse wrist curl exercise, the iEMG of extensor carpi ulnaris wearing condition was lower than the non-wearing condition. During wrist curl exercise, the iEMG of flexsor biceps brachii, carpi radialis wearing condition was lower than the non-wearing condition. During push-up exercise, the iEMG of triceps flexsor biceps brachii, carpi radialis, brachii, biceps brachii non-wearing condition was lower than the wearing condition.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.10
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• pp.1779-1785
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• 2006

This paper presents a method of virtual robot arm control by EMG pattern recognition using the proposed hybrid system. The proposed hybrid system is composed of the LVQ and the SOFM, and the SOFM is used for the preprocessing of the LVQ. The SOFM converts the high dimensional EMG signals to 2-dimensional data. The EMG measurement system uses three surface electrodes to acquire the EMG signal from operator. Six hand gestures can be classified sufficiently by the proposed hybrid system. Experimental results are presented that show the effectiveness of the virtual robot arm control by the proposed hybrid system based classifier for the recognition of hand gestures from EMG signal patterns.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.641-642
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• 1986

We present a method for the estimation of intramuscular electromyographic(EMG) signals from the given surface EMG signals. This method is based on representing the surface EMG signal as an autoregressive(AR) time model with a delayed intramuscular EMG signal as an input. The parameters of the time series model that transforms the intramuscular signal to the surface signal are identified. The identified model is then used in estimating the intramuscular signal from the surface signal.