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

摘要： 肌肉疲劳是肌肉在运动或劳动过程中遭受到过度使用或持续负荷后出现的生理现象，目前解析疲劳机制仍是复杂且多层次的研究难题。近年来，基于表面肌电信号的肌肉疲劳研究方法成为关注焦点，先进的信号处理技术和机器学习算法的应用提高了对表面肌电数据的解码能力，深化了对肌肉疲劳机制的理解，为提升运动表现、预防运动损伤以及改善康复治疗提供了重要的技术支持。对近几年基于表面肌电信号的肌肉疲劳研究进行了全面综述，首先，阐述了肌肉疲劳的定义以及目前常用的检测方法，并指出各种方法的特点和适用范围；其次，从时域、频域、时频域等线性特征和使用非线性参数的方式详细介绍了表征肌肉疲劳的肌电特征，同时探讨了这些特征的优点与局限性；再次，结合表征疲劳特征作为输入数据，对常用于肌肉疲劳的分类算法进行探究，从机器学习和深度学习算法两个方面准确归纳了各算法的适用条件和优劣势；最后，指出了现阶段肌肉疲劳研究所面临的挑战，并在提出可行解决方案的基础上，展望了未来的研究方向。

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