Title: A comparison of sEMG and MMG signal classification for automated muscle fatigue detection

Authors: Mohammed R. Al-Mulla; Francisco Sepulveda

Addresses: Department of Computing Sciences and Engineering, Kuwait University, Kuwait ' School of Computer Science and Electronic Engineering, University of Essex, UK

Abstract: This study compares the classification performance of both sEMG and MMG signal from fatiguing dynamic contraction of the biceps brachii. Commonly used statistical features are compared with a recently developed evolved pseudo-wavelet. Based on the literature, wavelet-based methods are a promising feature extraction technique for both types of signals (sEMG and MMG) during dynamic contractions. MMG results show that the evolved pseudo-wavelet improved the classification rate of muscle fatigue by 4.70 percentage points to 27.94 percentage points when compared to other standard wavelet functions, giving an average correct classification of 80.63%, with statistical significance (p < 0.05). For sEMG signals the evolved pseudo-wavelet improved the classification rate of muscle fatigue by 4.45 percentage points to 14.96 percentage points when compared to other standard wavelet functions (p < 0.05), giving an average correct classification of 87.90%. The comparison demonstrates that for both the sEMG and the MMG signal, the feature giving best classification results was the evolved pseudo-wavelet.

Keywords: localised muscle fatigue; electromyography; mechanomyography; wavelet analysis; pseudo-wavelets; Biceps Brachii; classification; genetic algorithm; dynamic contractions; sEMG electrodes; accelerometer; goniometer.

DOI: 10.1504/IJBET.2019.100697

International Journal of Biomedical Engineering and Technology, 2019 Vol.30 No.3, pp.277 - 293

Received: 30 Sep 2016
Accepted: 01 Feb 2017

Published online: 28 Jun 2019 *

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