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

Author

Listed:
  • Chunhai Cui

    (Yanching Institute of Technology, China)

  • Enqian Xin

    (Yanching Institute of Technology, China)

  • Meili Qu

    (Yanching Institute of Technology, China)

  • Shuai Jiang

    (Yanjing Institute of Technology, China)

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.

Suggested Citation

  • Chunhai Cui & Enqian Xin & Meili Qu & Shuai Jiang, 2021. "Fatigue and Abnormal State Detection by Using EMG Signal During Football Training," International Journal of Distributed Systems and Technologies (IJDST), IGI Global, vol. 12(2), pages 13-23, April.
    • Handle: RePEc:igg:jdst00:v:12:y:2021:i:2:p:13-23
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