• 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.63 no.2
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• pp.291-296
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• 2014

Most of the statistical signal analysis processed in the time domain and the frequency domain are based on the assumption that the signal is weakly stationary(wide sense stationary). Therefore, it is necessary to know whether the surface EMG signals processed in the statistical basis satisfy the condition of weak stationarity. The purpose of this study is to analyze the accuracy of the Run-test, modified Run-test, RA(reverse arrangement)-test, and modified RA-test for assessing surface EMG signal stationarity. Six stationary and three non-stationary signals were simulated by using sine wave, AR(autoregressive) modeling, and real surface EMG. The simulated signals were tested for stationarity using nine different methods of Run-test and RA-test. The results showed that the modified Run-test method2 (mRT2) classified exactly the surface EMG signals by stationarity with 100% accuracy. This finding indicates that the mRT2 may be the best way for assessing stationarity in surface EMG signals.

• Journal of Korean Physical Therapy Science
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• v.10 no.1
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• pp.83-89
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• 2003

An electromyogram (EMG) using surface electrodes is one of the indirect tests most frequently used to ascertain muscle fatigue. An EMG can be used in two ways. The first technique determines the root mean square (RMS), which reflects the amplitude of the EMG signal. The second technique determines the median and mean power frequencies through EMG power spectrum analysis. The purpose of this article is for determine the correlation between work and percent root mean square(%RMS) and between work and MDF of EMG based on muscle contractions. It is used the %RMS, which reflects the amplitude of the EMG signal For MDF, it is used the frequency power spectrum analysis method, which involves the fast Fourier transformation (FFT) of the original Signals.

• 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.

• Journal of Biomedical Engineering Research
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• v.17 no.2
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• pp.155-164
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• 1996

In the clinical environment, measurements of some characteristics of the skeletal muscle are currently used to assess the severity of a neuromuscular disease or in some cases to assist in making a diagnosis. But a quantitative method of evaluation has not yet been introduced satisfactorily. In this paper, the skeletal EMG(biceps muscle, masseter muscle) analysis has been processed both in the time and in the frequency domain by designing the digital signal processing system based on pentium PC and transputer (IMS 7805). The experiment have been performed in five normal subjects, and various parameters have been statistically tested and compare4 As a results, the effective parameters obtained for the evaluation of skeletal EMG electrical activity are turn analysis, MiTi, MiTa, IEMG, PDF in the time domain, and are mean frequency, median frequency, skewness, kurtosis, muscle fatigue slope in the frequency domain. The designed H/W and S/W in this study can be used effectively for the establishment of EMG data base and for clinical research.

• Journal of Biomedical Engineering Research
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• v.32 no.4
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• pp.328-335
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• 2011

For human factor engineering and wearable robot design, the quantitative assessment of physical workload is needed. Through measuring the surface EMG (sEMG) and analysis, the physical workload in overhead lifting posture is presented in quantitative manner. By normalizing sEMG activities with maximal voluntary contraction (MVC), the inter-subject variability is reduced. In all muscles, %MVC increased as the weight of lifting object increases. In anterior deltoid muscle, the %MVC was 3-4 times higher than the other muscles which imply that this muscle performs the major role in the overhead lifting posture. In fatigue analysis, %MVC and the mean frequency in muscle of anterior deltoid changed markedly when compared with other muscles. Through the suggested procedures and analysis, the physical workload for a specific posture can be represented in quantitative way but the clinical meaning for the value should be investigated further.

• 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.

• Journal of Biomedical Engineering Research
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• v.13 no.1
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• pp.9-18
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• 1992

This paper describes a quantitative analysis of electromyography (EMG) measured from seven subjects performing four seated dynamic tasks. EMG signals were mom- bored using 15 surface electrodes which were placed on selected trunk and lower extrmity muscles of the right side of the body. Each EMG signal was then processed through rectification, integration, and filtering. Based on the maximum level of the processed EMG, it was found that the trunk and ankle muscles play an important role on the postural control during the seated tasks.

• 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.