Continuous Wavelet Transform Analysis of Surface Electromyography for Muscle Fatigue Assessment on the Elbow Joint Motion

Triwiyanto Triwiyanto,Oyas Wahyunggoro,Hanung Adi Nugroho,Herianto Herianto

doi:10.15598/aeee.v15i3.2173

Abstract

Studying muscle fatigue plays an important role in preventing the risks associated with musculoskeletal disorders. The effect of elbow-joint angle on time-frequency parameters during a repetitive motion provides valuable information in finding the most accurate position of the angle causing muscle fatigue. Therefore, the purpose of this study is to analyze the effect of muscle fatigue on the spectral and time-frequency domain parameters derived from electromyography (EMG) signals using the Continuous Wavelet Transform (CWT). Four male participants were recruited to perform a repetitive motion (flexion and extension movements) from a non-fatigue to fatigue condition. EMG signals were recorded from the biceps muscle. The recorded EMG signals were then analyzed offline using the complex Morlet wavelet. The time-frequency domain data were analyzed using the time-averaged wavelet spectrum (TAWS) and the Scale-Average Wavelet Power (SAWP) parameters. The spectral domain data were analyzed using the Instantaneous Mean Frequency (IMNF) and the Instantaneous Mean Power Spectrum (IMNP) parameters. The index of muscle fatigue was observed by calculating the increase of the IMNP and the decrease of the IMNF parameters. After performing a repetitive motion from non-fatigue to fatigue condition, the average of the IMNF value decreased by 15.69% and the average of the IMNP values increased by 84%, respectively. This study suggests that the reliable frequency band to detect muscle fatigue is 31.10-36.19Hz with linear regression parameters of 0.979mV^2Hz^(-1) and 0.0095mV^2Hz^(-1) for R^2 and slope, respectively.

Highlights

In everyday life, when the limb performs an intensive repetitive motion, the muscle can experience muscle fatigue
The average of the Instantaneous Mean Frequency (IMNF) decreased by 15.69 % and the average of the Instantaneous Mean Power Spectrum (IMNP) increased by 84.14 %
Similar results were observed by Karthick and Krishnan which showed that the IMNF and IMDF of EMG signal decrease from non-fatigue to fatigue condition [6]

Summary

Introduction

In everyday life, when the limb performs an intensive repetitive motion, the muscle can experience muscle fatigue. When the muscle is in the fatigue condition, it is proved that the spectral parameters (frequency and amplitude) of the EMG signal will change [1]. A conventional method to measure the spectral parameters of the EMG signal is by utilizing the Fast Fourier Transform (FFT) method. In this case, the EMG signal, is assumed to be in the stationary condition which the muscle fatigue is determined by performing constant force or isometric contraction [2] on the subject’s limb. The muscle length changes [1] in accordance with the limb joint angle, and for this issue, the nonstationary characteristic of the EMG signal increases [3]

Objectives

Methods

Results

Conclusion