Evalution of neurodiagnostic insights for enhanced evaluation and optimization of badminton players' physical function via data mining technique

Abstract

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.