Adoption of Machine Learning Algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention

Abstract

In order to effectively prevent sports injuries caused by collisions in basketball training, realize efficient shooting, and reduce collisions, the machine learning algorithm was applied to intelligent robot for path planning in this study. First of all, combined with the basketball motion trajectory model, the sport recognition in basketball training was analyzed. Second, the mathematical model of the basketball motion trajectory of the shooting motion was established, and the factors affecting the shooting were analyzed. Thirdly, on this basis, the machine learning-based improved Q-Learning algorithm was proposed, the path planning of the moving robot was realized, and the obstacle avoidance behavior was accomplished effectively. In the path planning, the principle of fuzzy controller was applied, and the obstacle ultrasonic signals acquired around the robot were taken as input to effectively avoid obstacles. Finally, the robot was able to approach the target point while avoiding obstacles. The results of simulation experiment show that the obstacle avoidance path obtained by the improved Q-Learning algorithm is flatter, indicating that the algorithm is more suitable for the obstacle avoidance of the robot. Besides, it only takes about 250 s for the robot to find the obstacle avoidance path to the target state for the first time, which is far lower than the 700 s of the previous original algorithm. As a result, the fuzzy controller applied to the basketball robot can effectively avoid the obstacles in the robot movement process, and the motion trajectory curve obtained is relatively smooth. Therefore, the proposed machine learning algorithm has favorable obstacle avoidance effect when it is applied to path planning in basketball training, and can effectively prevent sports injuries in basketball activities.

Artificial Intelligence Technology in Basketball Training Action Recognition

“…Several research teams have begun to study how to improve the quality of basketball training through AI technology. Xu and Tang (2021) applied machine learning algorithms to the path planning of intelligent robots. Firstly, the movements in basketball training are identified and analyzed in combination with the basketball movement trajectory model.…”

“…Xu and Tang ( 2021 ) applied machine learning algorithms to the path planning of intelligent robots. Firstly, the movements in basketball training are identified and analyzed in combination with the basketball movement trajectory model.…”

“…Therefore, the proposed machine learning algorithm has a good obstacle avoidance effect when applied to path planning in basketball training. The application of this algorithm can effectively prevent sports injuries in basketball (Xu and Tang, 2021 ). Zhi and Jiang ( 2020 ) proposed a path planning algorithm based on behavioral module control, aiming at problems, such as the strong dependence of traditional algorithms on environmental information, the path planning of basketball robots in unknown environments, and the improvement of autonomous navigation safety.…”

Application of Artificial Intelligence in Basketball Sport

“…Basketball training robots are increasingly used in basketball training. In order to avoid injury caused by the collision between the mobile training robot and the athlete, Xu and Tang used ultrasonic signals of obstacles collected around the robot as input and applied an improved Q-learning algorithm based on machine learning, so that the robot can effectively avoid collisions with athletes during the training course and allow athletes to receive scientific and efficient training [42].…”

MPPTM: A Bio-Inspired Approach for Online Path Planning and High-Accuracy Tracking of UAVs

“…For dealing with complex problems, MRS has more advantages than SRS. Large numbers of researches state that the cooperation of MRS has been applied to more practical fields, such as services (Morita et al, 2018;Krizmancic et al, 2020), therapy (Ali et al, 2019;Mehmood et al, 2019), rescue (Queralta et al, 2020), training (Xu and Tang, 2021), and so on.…”