Design of the Exercise Load Data Monitoring System for Exercise Training Based on the Neural Network

Abstract

In order to monitor the sports load data of athletes in sports training, this paper studies the methods and systems of sports load monitoring and fatigue warning based on neural network technology. In this paper, the neural network parallel optimization algorithm based on big data is used to accurately estimate the motion load and intensity according to the determined motion mode and acceleration data, so as to realize the real-time monitoring of the exercise training. The results show that the value of η is usually small to ensure that the weight correction can truly follow the direction of the gradient descent. In this paper, 176 samples were extracted from the monitoring data collected by the “National Tennis Team Information Platform,” 160 of which were selected as training samples and the other 16 as test samples. Ant colony size M = 20. The minimum value Wmin of the weight interval is −2, and the maximum value Wmax is 2. The maximum number of iterations is set to 200. σ = 1; that is, only one optimal solution is retained. The domain is divided into 60 parts evenly; that is, r = 60. Generally, η can be taken as any number [28] between [10-3, 10], but the value is usually small to ensure that the weight correction can truly follow the direction of the gradient descent. In this paper, the value is 0.003. In the early warning stage of exercise fatigue, reasonable measurement units of exercise fatigue time were divided according to the characteristics of different exercise items. It is proved that the Bayesian classification algorithm can effectively avoid the sports injury caused by overtraining by warning the fatigue and preventing the sports injury caused by overtraining.