Evalution of neurodiagnostic insights for enhanced evaluation and optimization of badminton players' physical function via data mining technique.
| Autor: | Xu F; Department of Physical Education, Bengbu University, Bengbu 233030, China. Electronic address: wsxufeng@126.com., Zhu W; Department of Physical Education, Hefei University of Technology, Hefei 230009, China. Electronic address: zhuwuyi926@163.com. |
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| Jazyk: | angličtina |
| Zdroj: | SLAS technology [SLAS Technol] 2024 Aug; Vol. 29 (4), pp. 100138. Date of Electronic Publication: 2024 Apr 30. |
| DOI: | 10.1016/j.slast.2024.100138 |
| Abstrakt: | This research presents a novel method for objectively evaluating college badminton players' physical function levels. It examines current evaluation methods before proposing a novel model that combines Particle Swarm Optimization (PSO) with Backpropagation (BP) neural networks and data mining. The model establishes an evaluation index system that considers physical form, function, quality, and neural mechanisms. The study uses PSO-BP neural networks to adjust indicator weights for more accurate ratings. This recurrent improvement reduces errors while increasing prediction ability, resulting in accurate assessments of athletes' physical talents and neurological insights. The model's efficiency is proved by low mistakes and high accuracy results, which are critical for training optimization and injury avoidance. The combination of PSO optimization and BP neural networks offers robustness across various athlete profiles and training scenarios. This method improves physical function evaluation in badminton and has wider implications for sports science and performance analytics. This study uses bio-inspired computing and machine learning to emphasize the relevance of data-driven techniques in enhancing athlete assessments for better training outcomes and general well-being. Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2024. Published by Elsevier Inc.) |
| Databáze: | MEDLINE |
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