Review of Application of Surface Electromyography Signals in Muscle Fatigue Research

• Published in: Jisuanji kexue yu tansuo Vol. 18; no. 9; pp. 2261 - 2275
• Main Author: FANG Boru, QIU Dawei, BAI Yang, LIU Jing
• Format: Journal Article
• Language: Chinese
• Published: Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 01.09.2024
• Subjects: muscle fatigue; surface electromyographic; electromyographic features; machine learning; deep learning algorithms
• ISSN: 1673-9418
• DOI: 10.3778/j.issn.1673-9418.2312042

Muscle fatigue is a physiological phenomenon that occurs when muscles are overused or continuously loaded during exercise or labor. Currently, analyzing the fatigue mechanism is still a complex and multi-layered research problem. In recent years, research methods focusing on surface electromyographic (sEMG) signals have garnered significant attention. The application of advanced signal processing techniques and machine learning algorithms has enhanced the precision of interpreting surface electromyographic data, deepening our understanding of the mechanisms underlying muscle fatigue. This, in turn, provides crucial scientific support for improving athletic performance, preventing sports injuries, and enhancing rehabilitation treatments.This comprehensive review of muscle fatigue research based on surface electromyographic signals covers various aspects. First, the definition of muscle fatigue and currently commonly used detection methods are explained, and the characteristics and application scope of various