APPLICATION OF BACK PROPAGATION NEURAL NETWORK IN SPORTS FATIGUE INDICATORS

APLICAÇÃO DA REDE NEURAL DE RETROPROPAGAÇÃO EM INDICADORES DE FADIGA ESPORTIVA

APLICACIÓN DE LA RED NEURONAL DE RETROPROPAGACIÓN EN INDICADORES DE FATIGA DEPORTIVA

ABSTRACT

Introduction

High-intensity rehabilitation training will produce exercise fatigue.

Objective

A backpropagation (BP) network neural algorithm is proposed to predict sports fatigue based on electromyography (EMG) signal images.

Methods

The principal component analysis algorithm is used to reduce the dimension of EMG signal features. The knee joint angle is estimated by the regularized over-limit learning machine algorithm and the BP neural network algorithm.

Results

The RMSE value of the regularized over-limit learning machine algorithm is lower than that of the BP neural network algorithm. At the same time, the ρ value of the regularized over-limit learning machine algorithm is closer to 1, indicating its higher accuracy.

Conclusions

The model training time of the regularized over-limit learning machine algorithm has been greatly reduced, which improves efficiency. Level of evidence II; Therapeutic studies - investigation of treatment results.

Exercise,high-intensity; Fatigue; Knee Joint