Intelligent Recognition of Athlete's Erroneous Movements in Sports Training Under Artificial Intelligence Technology

To address the challenge of inaccuracies in human assessment of movement accuracy, the author suggests incorporating artificial intelligence technology into sports training. Leveraging the advancements in computer vision, this method involves athletes intentionally making incorrect movements during training sessions. By integrating intelligent recognition capabilities, particularly in identifying erroneous actions, this approach aims to enhance the overall accuracy of recognizing and rectifying flawed movements in sports training. So the author first introduced computer vision technology, which can perform digital analysis on captured images and has strong application performance. Then, the feature extraction of athlete movements is analyzed, and Bayesian algorithms are used to identify erroneous movements, resulting in a three-dimensional visual detection model. Finally, experimental research was conducted on the 3D visual detection model. The experimental results show that the accuracy of the algorithms proposed by the author is all greater than 90%, while the accuracy of traditional methods is only 70% -76%. While ensuring accuracy, it can ensure a high accuracy. The experimental results show that the method proposed by the author has significantly improved accuracy compared to conventional methods, and confirms its feasibility in identifying erroneous behaviors.