Effectiveness of the Wavelet Transform on the Surface EMG to Understand the Muscle Fatigue During Walk

Muscle fatigue is a long lasting reduction of the ability to contract and it is the condition when produced force is reduced. Walking fast can cause muscle fatigue, which is unhealthy and it is incurable when the level of fatigue is high. Muscle fatigue during walk can be determined using several spectral variables. The amplitude and frequency of the surface EMG signal provide a more accurate reflection of motor unit pattern among these spectral variables. This research reports on the effectiveness of Empirical mode decomposition (EMD) and wavelet transform based filtering method applied to the surface EMG (sEMG) signal as a means of achieving reliable discrimination of the muscle fatigue during human walking exercise. In this research, IAV, RMS and AIF values were used as spectral variable. These spectral variables extensively identifies the difference between fatigue and normal muscle when using EMD method compared with other different wavelet functions (WFs). The result shows that the sEMG amplitude and frequency momentously changes from rest position to maximum contraction position.

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Intramuscular and surface electromyogram changes during muscle fatigue

Journal of Applied Physiology ◽

10.1152/jappl.1986.60.4.1179 ◽

1986 ◽

Vol 60 (4) ◽

pp. 1179-1185 ◽

Cited By ~ 258

Author(s):

T. Moritani ◽

M. Muro ◽

A. Nagata

Keyword(s):

Muscle Fatigue ◽

Biceps Brachii ◽

Surface Emg ◽

Firing Frequency ◽

Voluntary Contraction ◽

Mean Power ◽

Mean Power Frequency ◽

Surface Electromyogram ◽

Spike Amplitude ◽

Frequency Power

Twelve male subjects were tested to determine the effects of motor unit (MU) recruitment and firing frequency on the surface electromyogram (EMG) frequency power spectra during sustained maximal voluntary contraction (MVC) and 50% MVC of the biceps brachii muscle. Both the intramuscular MU spikes and surface EMG were recorded simultaneously and analyzed by means of a computer-aided intramuscular spike amplitude-frequency histogram and frequency power spectral analysis, respectively. Results indicated that both mean power frequency (MPF) and amplitude (rmsEMG) of the surface EMG fell significantly (P less than 0.001) together with a progressive reduction in MU spike amplitude and firing frequency during sustained MVC. During 50% MVC there was a significant decline in MPF (P less than 0.001), but this decline was accompanied by a significant increase in rmsEMG (P less than 0.001) and a progressive MU recruitment as evidenced by an increased number of MUs with relatively large spike amplitude. Our data suggest that the surface EMG amplitude could better represent the underlying MU activity during muscle fatigue and the frequency powers spectral shift may or may not reflect changes in MU recruitment and rate-coding patterns.

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A Real-Time Algorithm to Estimate Shoulder Muscle Fatigue Based on Surface EMG Signal For Static and Dynamic Upper Limb Tasks

10.1109/embc46164.2021.9630702 ◽

2021 ◽

Author(s):

Marianne Boyer ◽

Laurent Bouyer ◽

Jean-Sebastien Roy ◽

Alexandre Campeau-Lecours

Keyword(s):

Muscle Fatigue ◽

Real Time ◽

Upper Limb ◽

Time Algorithm ◽

Surface Emg ◽

Emg Signal ◽

Shoulder Muscle ◽

Surface Emg Signal

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Techniques for Decomposition of EMG Signals

Clinical Technologies ◽

10.4018/978-1-60960-561-2.ch216 ◽

2011 ◽

pp. 467-477

Author(s):

Arun Kumar Wadhwani ◽

Sulochana Wadhwani

Keyword(s):

Pattern Recognition ◽

Wavelet Transform ◽

Muscle Fatigue ◽

Cross Correlation ◽

Neuromuscular Disorders ◽

Clinical Importance ◽

Neuromuscular Control ◽

Diagnostic Information ◽

Self Organizing Map ◽

Statistical Pattern

The information extracted from the EMG recordings is of great clinical importance and is used for the diagnosis and treatment of neuromuscular disorders and to study muscle fatigue and neuromuscular control mechanism. Thus there is a necessity of efficient and effective techniques, which can clearly separate individual MUAPs from the complex EMG without loss of diagnostic information. This chapter deals with the techniques of decomposition based on statistical pattern recognition, cross-correlation, Kohonen self-organizing map and wavelet transform.

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