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Review
. 2022 Aug 11:16:893275.
doi: 10.3389/fnsys.2022.893275. eCollection 2022.

Application of Surface Electromyography in Exercise Fatigue: A Review

Jiaqi Sun  1 , Guangda Liu  1 , Yubing Sun  1 , Kai Lin  1 , Zijian Zhou  1 , Jing Cai  1
Affiliations
Review

Application of Surface Electromyography in Exercise Fatigue: A Review

Jiaqi Sun et al. Front Syst Neurosci. .

Abstract

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.

Keywords: classification; exercise fatigue; feature extraction; machine learning; sEMG.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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