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Journal Article

Differentiating Muscle Fatigue and Nonfatigue Conditions Using Surface EMG Signals and Zhao-Atlas-Marks Based Time-Frequency Distribution.

01 Jan 2015-Biomedical sciences instrumentation (Biomed Sci Instrum)-Vol. 51, pp 115-121
TL;DR: An attempt has been made to differentiate the sEMG signals under muscle non-fatigue and fatigue conditions using Zhao-Atlas-Marks (ZAM) based time frequency distribution, and the results show that IMDF and IMNF are distinct for muscleNonFatigue and Fatigue conditions.
Abstract: Muscle fatigue is a neuromuscular condition where muscles fail to generate the required force. It occurs in normal as well as abnormal subjects. The analysis of muscle fatigue plays a significant role in the field of clinical studies, myo-electric control, ergonomics and sports biomechanics. In this work, an attempt has been made to differentiate the sEMG signals under muscle non-fatigue and fatigue conditions using Zhao-Atlas-Marks (ZAM) based time frequency distribution. For this purpose, sEMG signals are recorded from fifty healthy volunteers during isometric contractions under well defined protocol. The acquired signals are preprocessed and subjected to ZAM based time-frequency analysis. The time-frequency based features such as instantaneous median frequency (IMDF) and instantaneous mean frequency (IMNF) are extracted from the time-frequency spectrum. The results show that IMDF and IMNF are distinct for muscle non-fatigue and fatigue conditions. Further, more number of frequency components are observed in the time-frequency spectrum of signals recorded in nonfatigue conditions. The t-test performed on these features has shown significant difference (p<0.01) in between non-fatigue and fatigue conditions. Thus the study seems to be useful for the analysis of various neuromuscular conditions.
Citations
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Journal ArticleDOI
TL;DR: The rehabilitation training bed is designed by combining the rehabilitation training with the motion prediction based on the surface myoelectric signal, which can recognize the tilt of the upper body in different directions and provide corresponding assistance to the elderly.
Abstract: Rehabilitation training can effectively help the elderly recover their self‐care state and enhance physical fitness. As surface electromyography analysis is effective to recognize motion intention, researchers use it to develop prosthetic limb operations. In this article, the rehabilitation training bed is designed by combining the rehabilitation training with the motion prediction based on the surface myoelectric signal, which can recognize the tilt of the upper body in different directions and provide corresponding assistance to the elderly. After collecting EMG signal, the effective signal was dimensional reduction, mapped by linear discriminant analysis. To train and recognize the EMG‐motion mapping relationship, we used a recurrent neural network called nonlinear autoregressive with exogenous input model and used a 360° tilt prediction experiment on the upper body. Results showed that the root mean squared error and the error autocorrelation coefficient were relatively low, and the tilt degree of the experimenter was highly matched.

6 citations

Proceedings ArticleDOI
28 Jul 2020
TL;DR: In this article, a model to fit the fatigue and non fatigue surface electromyography (sEMG) signals using sum of sines is proposed, and the root mean square error (RMSE) of fatigue condition reduced by 4 from sin7 model to sin8 model.
Abstract: Muscle fatigue is a common experience for all age groups. In this work a model to fit the fatigue and non fatigue surface electromyography (sEMG) signals using sum of sines is proposed. Signals are recorded from Biceps Brachii muscle of five healthy volunteers until fatigue using a well defined protocol. The fatigue and non fatigue conditions are analysed separately by non linear dynamical model. The sum of sine model is selected for fitting the signals. The sin7 model is found to be the best non linear fit for non fatigue condition and sin8 for fatigue condition. The Root Mean Square Error (RMSE) of fatigue condition reduced by 4 from sin7 model to sin8 model. Also the fatigue signal tends to be periodic than non fatigue signal. This method may be further extended to the non linear analysis of muscular disorders.

5 citations

Journal ArticleDOI
TL;DR: Investigating the effect of different rest intervals within paired sets (PS) on total work and training volume, efficiency, training volume load/session duration time, and myoelectric activity found short intra-set rest intervals (60s) within PS may be a potential alternative for increasing the volume load.

3 citations

Proceedings ArticleDOI
01 Jul 2020
TL;DR: An attempt has been made to distinguish between nonfatigue and fatigue conditions in surface Electromyography (sEMG) signal using the time frequency distribution obtained from analytic Bump Continuous Wavelet Transform.
Abstract: In this study, an attempt has been made to distinguish between nonfatigue and fatigue conditions in surface Electromyography (sEMG) signal using the time frequency distribution obtained from analytic Bump Continuous Wavelet Transform. For the analysis, sEMG signals from biceps brachii muscle of 22 healthy subjects are acquired during isometric contraction protocol. The signals acquired is preprocessed and partitioned into ten equal segments followed by the decomposition of selected segments using analytic Bump wavelets. Further, Singular Value Decomposition is applied to the time frequency distribution matrix and the maximum singular value and entropy feature for each segment are obtained. The usefulness of both the features is estimated using the Wilcoxon sign rank test that gives higher significance with a p < .00001. It is observed that the proposed method is capable of analyzing the fatigue regions in sEMG signals.

1 citations


Cites methods from "Differentiating Muscle Fatigue and ..."

  • ...The isometric exercise protocol involves maintaining the upper arm perpendicular to the ground and the lower arm is maintained at the angle of 90° from the vertical axis [17, 18]....

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  • ...SVD is the optimal feature used to decompose the large TF matrix in the least square sense and packs the maximum signal energy into few coefficients as possible [18]....

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Proceedings ArticleDOI
19 May 2023
TL;DR: In this article , the fiber type proportion of lower limb muscles such as gastrocnemius lateralis (GL) and soleus (SOL) using surface electromyography was investigated.
Abstract: Muscle fiber composition is a key performance-determining factor in athletics. The proportion of slow-twitch fibers (STF) and fast-twitch fibers (FTF) may differ greatly in muscles due to factors such as genetics, training-induced changes, or underlying pathological conditions. This study investigates the fiber type proportion of lower limb muscles such as gastrocnemius lateralis (GL) and soleus (SOL) using surface electromyography. Isometric signals are recorded from both muscles during calf raise exercises. First and last one second epochs of the recorded signals correspond to non-fatigue (NF) and fatigue (F) zones respectively and are analyzed using smoothed pseudo Wigner-Ville distribution (SPWVD). Two features namely spectral entropy (SE) and peak frequency (PF) are extracted from both zones. During fatigue, SE shows a decline of 4.87% in GL and an increase of 3.7% in SOL. Similarly, a 65.3% decrease in PF in GL and 41% increase in SOL is observed. The reduction of SE and PF in GL may be due to the fatigue of FTF whereas the increment of these features in SOL might be due to the greater proportion of STF. The decrease of SE in GL reflects the reduction of complexity with fatigue. Hence, the extracted features are found effective in identifying the fiber type proportion of these muscles. The proposed approach can be used for fiber-type assessment in sports science and biomedicine.