An Analysis Method of Exercise Load in Physical Training Based on Radial Basis Neural Network Model

Abstract

In the training process of professional athletes, to optimize the training plan and make the athletes play the best competitive state at a special time point, it is usually achieved by controlling the training load and active and effective recovery process. For the general public, daily exercise is mainly for physical fitness and physical rehabilitation. Whether it is a professional athlete or the general public, there are times when injuries occur during sports. The appropriate degree of exercise load varies from person to person. According to different sports, people’s exercise suitability is also different. Therefore, it is meaningful to analyze and monitor the exercise load of the athlete during exercise. This paper proposes to use radial basis neural network (RBFNN) in the analysis of sports f-load of athletes. RBFNN is a kind of neural network that relies on error backpropagation for parameter adjustment, and its convergence speed is slow. When the data dimension is large and the amount of data is large, it will affect the classification accuracy of the data. For this reason, this paper integrates the gray wolf optimization algorithm (GWO) and RBFNN, and applies GWO to the initial value determination of weights and thresholds, which can effectively reduce the adjustment range of parameters and improve the accuracy of data classification. The model can more accurately analyze the exercise load state of athletes during exercise. The experimental results show that the high-quality heart rate data can be classified based on the model used in this paper, so that the exercise load state can be correctly judged. This has a good reference value for the analysis of exercise load during sports training and the next monitoring.