Title: Injury prediction analysis of college basketball players based on FMS scores

Authors: Mingtao Wang; Zhen Pei

Addresses: College of Physical Education and Health, Xinxiang Vocational and Technical College, Xinxiang, Henan, China ' College of Physical Education and Health, Xinxiang Vocational and Technical College, Xinxiang, Henan, China

Abstract: It is inevitable for basketball players to have physical injury in sports. Reducing basketball injury is one of the main aims of the study of basketball. In view of this, this paper proposes a monocular vision and FMS injury prediction model for basketball players. Aiming at the limitations of traditional FMS testing methods, this study introduces intelligent machine learning methods. In this study, random forest algorithm was introduced into OpenPose network to improve model node occlusion, missed detection or false detection. In addition, to reduce the computational load of the network, the original OpenPose network was replaced by a lightweight OpenPose network. The experimental results show that the average processing time of the proposed model is about 90 ms, and the output video frame rate is 10 frames per second, which can meet the real-time requirements. This study analysed the students participating in the basketball league of the College of Sports Science of Nantong University, and the results confirmed the accuracy of the injury prediction of college basketball players based on FMS scores. It is hoped that this study can provide some reference for the research of injury prevention of basketball players.

Keywords: FMS score; basketball players; damage prediction; lightweight OpenPose network; random forest.

DOI: 10.1504/IJWMC.2024.142087

International Journal of Wireless and Mobile Computing, 2024 Vol.27 No.4, pp.346 - 355

Received: 30 Aug 2023
Accepted: 28 Jan 2024
Published online: 07 Oct 2024 *

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