Research on sports simulation and fatigue characteristics of athletes based on machine learning

Issue title: Fuzzy System for Economy Back on Track

Guest editors: Anand Paul, Simon K.S. Cheung, Chiung Ching Ho and Sadia Din

Article type: Research Article

Authors: Li, Guangqi^{; *}

Affiliations: School of Physical Education, Northeast Normal University, Jilin Changchun, China

Correspondence: [*] Corresponding author. Guangqi Li, School of Physical Education, Northeast Normal University, China. E-mail: Leeqswdef@163.com.

Abstract: The research on the fatigue characteristics of athletes has a certain role in promoting the development of sports. In order to detect fatigue more accurately in the state of human fatigue, this article uses a method of fusing characteristic information of many physiological parameters related to fatigue to design a multi-physical parameter-based exercise fatigue recognition method with high research value and significance. Moreover, this study combines machine learning technology to construct a dynamic fatigue detection system based on BP neural network and multiple physiological parameters. In addition, this study uses samples to construct a BP neural network and achieves dynamic detection of fatigue through multiple physiological parameters. Finally, by constructing controlled trials, fatigue is predicted. The results show that the predicted output of the fatigue value is in good agreement with the expected output, and the research method has certain practical effects.

Keywords: Machine learning, athletes, fatigue characteristics, exercise simulation

DOI: 10.3233/JIFS-189574

Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7531-7542, 2021