Abstract
Sports injury is a common problem in athletes’ training. The sports injury assessment model is a physical method to determine the sports injury attributes of specific parts by predicting and evaluating the risk of sports injury. In this paper, we use a neural network to realize big data analysis of sports injury data. Big data network is a method of capturing Internet information by means of cloud computing, which is usually used in the construction of Wan and LAN. This paper analyzes the source of sports risk and the main injury factors, designs the sports injury estimation model based on big data analysis, establishes a new assessment model based on RBF neural network, and builds the big data network environment required for the model operation by improving the topological structure, combining big data and deep neural network. In the built environment, the risk assessment of sports injury can be completed by determining the risk source and identifying the risk factors. The realization of the model can be constrained by the uncertainty conditions so that it can achieve a good operation state.
Highlights
As the “mother of sports”, track and field plays an important role in competitive sports
By combining the early warning evaluation results of coaches, experts, and linear model, the final early warning level is obtained and provided to the sample database for use. is paper analyzes the sources of sports risk and main injury factors, designs a sports injury estimation model based on big data analysis, and establishes a new sports injury assessment model based on Radial basis function (RBF) neural network
(6) e trained RBF neural network is used to evaluate the relevant information submitted by athletes and output the correct risk warning results
Summary
As the “mother of sports”, track and field plays an important role in competitive sports. In the research of sports injury risk early warning, predecessors have done a lot of research on sports injury causing factors and Complexity established qualitative models, but there is no quantitative evaluation research. E demand data of the sample database includes all the related factors information of sports injury risk early warning and the corresponding warning level. Erefore, this project first establishes a linear dynamic chain model, using the collected information of sports injury-related factors to determine the early warning level quantitatively. Is paper analyzes the sources of sports risk and main injury factors, designs a sports injury estimation model based on big data analysis, and establishes a new sports injury assessment model based on RBF neural network. By improving the topological structure, combining big data and deep neural networks, the big data network environment required for model operation is built
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