Judgment of Athlete Action Safety in Sports Competition Based on LSTM Recurrent Neural Network Algorithm

Abstract

Athlete injury has always been an important factor that plagues sports. In order to reduce the probability of athletes’ sports injury and improve the judgment of athletes’ action safety, the inherent laws of sports actions are fully excavated, the development of action safety is promoted, and learners and instructors are caused to fully understand the safety of actions. This study uses the LSTM (long short-term memory) cyclic neural network algorithm to judge the safety of athletes in sports competitions. The experiment verifies the effectiveness of the LSTM cyclic neural network algorithm in basketball segmentation and recognition. Sports injury is one of the important factors affecting the performance of all sports, and the problem of athletes’ injury is worrying, so it is very necessary to effectively prevent potential sports injuries. Through the investigation of different professional athletes, the LSTM cyclic neural network algorithm is used for the whole process of extracting an independent motion action including continuous actions. It is used to distinguish key postures and nonkey postures in an action, and to judge the correctness of the action. Basketball skills here are mainly the movements of basic skills such as moving, passing the ball, dribbling, shooting, breaking with the ball, personal defense, grabbing the ball, stealing the ball, and grabbing the ball. The research results prove that the LSTM recurrent neural network algorithm has a good effect on the safety of athletes. For athletes, 41.9% of people can improve the safety of their movements by strengthening strength training.