Title: Identifying illegal actions method of basketball players based on improved genetic algorithm

Authors: Zhenyu Zhu

Addresses: Department of Police Sports Teaching and Research, Jilin Police College, Changchun, 130000, China

Abstract: In order to reduce the time required for identifying athlete violations and improve the recognition rate, this paper proposes a basketball player violation recognition method based on an improved genetic algorithm. Firstly, the surface electromyographic signals of athletes are collected using a wireless sEMG signal acquisition device. Secondly, the location of signal acquisition is determined and the time-domain features of the signal is extracted. Then, a composite filter is used to denoise the signal. Finally, the genetic algorithm is improved by combining support vector machines to design an action recognition classifier, which outputs the results of illegal action recognition through the recognition classifier. Through experiments, it can be seen that this method can effectively improve the recognition rate by 9.44%, and within 0.5 minutes, the recognition effect of basketball players' illegal actions is good.

Keywords: improved genetic algorithm; wavelet transform threshold denoising; action recognition classifier; surface electromyographic signal; sEMG.

DOI: 10.1504/IJBM.2024.140772

International Journal of Biometrics, 2024 Vol.16 No.5, pp.419 - 430

Received: 27 Jun 2023
Accepted: 14 Sep 2023
Published online: 02 Sep 2024 *

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