表面肌电信号在肌肉疲劳研究中的应用综述

doi:10.3778/j.issn.1673-9418.2312042

摘要/Abstract

摘要： 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 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 review of muscle fatigue research based on surface electromyographic signals covers various aspects. Firstly, the definition of muscle fatigue and current commonly used detection methods are explained, and the characteristics and application scope of various methods are pointed out. Secondly, the EMG characteristics that characterize muscle fatigue are introduced in detail from linear characteristics such as time domain, frequency domain, time-frequency domain and the use of nonlinear parameters, and the advantages and limitations of these characteristics are also discussed. Thirdly, combining fatigue characteristics as input data, the classification algorithms commonly used for muscle fatigue are explored, and the applicable conditions, advantages and disadvantages of each algorithm are accurately summarized from the aspects of machine learning and deep learning algorithms. Finally, the challenges faced by muscle fatigue research at this stage are pointed out, and on the basis of proposing feasible solutions, future research directions are prospected.

关键词: 肌肉疲劳, 表面肌电, 肌电特征, 机器学习, 深度学习算法

Abstract: 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 methods are pointed out; Secondly, the EMG characteristics that characterize muscle fatigue are introduced in detail from linear characteristics such as time domain, frequency domain, time-frequency domain and the use of nonlinear parameters, and the advantages and limitations of these characteristics are also discussed; Thirdly, combining fatigue characteristics as input data, the classification algorithms commonly used for muscle fatigue are explored, and the applicable conditions, advantages and disadvantages of each algorithm are accurately summarized from the aspects of machine learning and deep learning algorithms; Finally, the challenges faced by muscle fatigue research at this stage are pointed out, and on the basis of proposing feasible solutions, the future research directions are prospected.

Key words: muscle fatigue, surface electromyographic, electromyographic features, machine learning, deep learning algorithms

方博儒, 仇大伟, 白洋, 刘静. 表面肌电信号在肌肉疲劳研究中的应用综述[J]. 计算机科学与探索, 2024, 18(9): 2261-2275.

FANG Boru, QIU Dawei, BAI Yang, LIU Jing. Review of Application of Surface Electromyography Signals in Muscle Fatigue Research[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(9): 2261-2275.

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