Athlete athletic injury risk early warning method

Abstract

The invention discloses an athlete athletic injury risk early warning method. On the bases of reference to models provided by foreign scholars and comprehensive analysis of track and field athlete athletic injury factors, a track and field athlete motion injury risk early warning injury factor dynamic chain model is provided, based on the model, corresponding factors are selected from an athlete risk early warning database, and a track and field athlete motion injury risk early warning injury factor dynamic chain quantitative model is established through the analytic hierarchy process. Index data are discretized according to an SOM neutral network discretization method, a decision table is simplified according to a method based on a discernibility matrix in a rough set, an RBF neutral network is established based on the simplified decision table, the RBF neutral network is trained, and finally a correct diagnosis result is acquired. By the adoption of the method, accurate early warning can be conducted on athletic injuries, the athlete athletic injury risk grade is effectively predicted, and treatment and prevention of the athletic injuries are facilitated.

Description

technical field [0001] The invention relates to a method for early warning of athlete's sports injury risk based on rough set-neural network. Background technique [0002] In track and field sports, there are many factors related to sports injuries, including internal injury factors (age, gender, injury history, etc.), external injury factors (field, equipment, weather, etc.), inducing stimulus conditions (training volume, competition density, etc.) etc.), how to find out the relationship between them and the risk of sports injury among so many factors is a daunting task. Moreover, among these factors, there are not only indicators that can be accurately measured, such as physiological and biochemical indicators, but also some qualitative indicators, such as past medical history, body shape characteristics, etc., and the indicators measured by each athlete in the past data are not the same. A comprehensive assessment of sports injury risk is a very difficult task. Radial B...

AI Technical Summary

Problems solved by technology

However, the use of neural networks for sports injury risk assessment has problems such as too many selected feature parameters, too large a neural network scale, and a long learning and training time, which seriously affects the actual use effect and real-time performance of the model.

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