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Fatigue and Abnormal State Detection by Using EMG Signal During Football Training

Fatigue and Abnormal State Detection by Using EMG Signal During Football Training

Chunhai Cui, Enqian Xin, Meili Qu, Shuai Jiang
Copyright: © 2021 |Volume: 12 |Issue: 2 |Pages: 11
ISSN: 1947-3532|EISSN: 1947-3540|EISBN13: 9781799861737|DOI: 10.4018/IJDST.2021040102
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MLA

Cui, Chunhai, et al. "Fatigue and Abnormal State Detection by Using EMG Signal During Football Training." IJDST vol.12, no.2 2021: pp.13-23. http://doi.org/10.4018/IJDST.2021040102

APA

Cui, C., Xin, E., Qu, M., & Jiang, S. (2021). Fatigue and Abnormal State Detection by Using EMG Signal During Football Training. International Journal of Distributed Systems and Technologies (IJDST), 12(2), 13-23. http://doi.org/10.4018/IJDST.2021040102

Chicago

Cui, Chunhai, et al. "Fatigue and Abnormal State Detection by Using EMG Signal During Football Training," International Journal of Distributed Systems and Technologies (IJDST) 12, no.2: 13-23. http://doi.org/10.4018/IJDST.2021040102

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Abstract

This paper proposes to monitor and recognize the fatigue state during football training by analyzing the surface electromyography (EMG) signals. The surface electromyography (EMG) signal is closely connected with the state during sports and training. First, power frequency interference, motion artifacts, and baseline drift in the surface electromyography (EMG) signal are removed; second, the authors extract 6 features: rectified average value (ARV), integrated electromyography myoelectric value (IEMG), root mean square of electromyography value (RMS), median frequency (MF), average power frequency (MPF), and electromyography power (TP) to represent the surface electromyography (EMG) signal; lastly, the extracted features are input into a one-class support vector machine to determine whether the player has been fatigued and are input into a weighted support vector machine to determine the degree of fatigue if the player has been fatigued. The experimental results show that more than 95% of the fatigue state can be recognized by surface electromyography (EMG) signal.

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