Early Warning Model of Track and Field Sports Injury Based on RBF Neural Network Algorithm

Abstract

Track and field events are the foundation of sports events. To learn other sports events well, you must also have a certain grasp of track and field events. As the movements of each track and field event become more and more complex, the competition becomes more and more intense, and the technical requirements are getting higher and higher. Therefore, sports injuries are gradually increasing. The prevention of sports injuries is a hot spot in sports medicine. Studies have found that as many as twenty factors can directly or indirectly cause track and field sports injuries. Based on the collected data on the injury-causing factors of elite athletes’ track and field sports, and on the basis of in-depth research on mobile data processing technology and early warning risks, a mobile early warning system and radial system using sports injury risk early warning algorithms, functional design and architecture are proposed. The basis function (RBF) neural network algorithm is released. Research on Early Warning Model of Sports Injury. This article analyzes and evaluates different hazard factors through the method of literature data, expert review and extensive analysis. In the investigation, the various causes of the injury were analyzed in detail, and it was found that most sports injuries were caused by students’ lack of attention, technical errors, insufficient preparation activities, etc., and finally proposed countermeasures to solve the problem, so that the sports injury situation was obtained improve.