Sun Jiaqi, Liu Guangda, Sun Yubing, Lin Kai, Zhou Zijian, Cai Jing
College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China.
Front Syst Neurosci. 2022 Aug 11;16:893275. doi: 10.3389/fnsys.2022.893275. eCollection 2022.
Exercise fatigue is a common physiological phenomenon in human activities. The occurrence of exercise fatigue can reduce human power output and exercise performance, and increased the risk of sports injuries. As physiological signals that are closely related to human activities, surface electromyography (sEMG) signals have been widely used in exercise fatigue assessment. Great advances have been made in the measurement and interpretation of electromyographic signals recorded on surfaces. It is a practical way to assess exercise fatigue with the use of electromyographic features. With the development of machine learning, the application of sEMG signals in human evaluation has been developed. In this article, we focused on sEMG signal processing, feature extraction, and classification in exercise fatigue. sEMG based multisource information fusion for exercise fatigue was also introduced. Finally, the development trend of exercise fatigue detection is prospected.
运动疲劳是人类活动中常见的生理现象。运动疲劳的发生会降低人体的功率输出和运动表现，并增加运动损伤的风险。作为与人类活动密切相关的生理信号，表面肌电图（sEMG）信号已被广泛应用于运动疲劳评估。在表面记录的肌电信号的测量和解释方面已经取得了很大进展。利用肌电特征来评估运动疲劳是一种切实可行的方法。随着机器学习的发展，sEMG信号在人体评估中的应用也得到了发展。在本文中，我们重点关注运动疲劳中的sEMG信号处理、特征提取和分类。还介绍了基于sEMG的运动疲劳多源信息融合。最后，展望了运动疲劳检测的发展趋势。
