Research and Implementation of an Artificial Intelligence-Kinect Integrated Intelligent Movement Teaching System for the Prevention and Control of Sports Injuries in High School Students

To address core pain points in real-time, accurate evaluation of movement posture, lack of personalized guidance, and insufficient early warning of injury risk in high school physical education, this study develops Smart Physical Observation and Safety Education (SPOSE). SPOSE deeply fuses Red Green Blue-Depth (RGB-D) data streams from Kinect V2 and constructs an end-to-end deep learning framework. Three-dimensional skeletal features are extracted via depthwise separable convolutions. A dynamic graph is built based on Graph Theory Matrix Analysis (GTMA); subsequently, spatiotemporal information is modeled by integrating graph convolution and temporal gated convolution. Memory prototype matching and collaborative attention mechanisms are introduced to generate personalized standards. These components facilitate action scoring, corrective feedback, and injury early warning. Furthermore, the study establishes a precise intelligent movement teaching and injury prevention system. The system models the spatial structural relationships and temporal variations of human movements to achieve intelligent analysis of motion postures and potential injury risks. In Study 1, the generalization ability of the system is verified utilizing the public NTU RGB+D and NTU RGB+D 120 datasets. The accuracy of cross-protocol recognition reaches 93.3%; meanwhile, movement scores are highly correlated with expert evaluations (r=0.912, p<0.001). In Study 2, skeleton sequences are generated from public videos to verify the adaptability to real teaching scenarios. The intra-group variance of individual evaluations is reduced by approximately 60% compared with the fixed template method. Comprehensive experiments demonstrate that the system’s early warning window for typical injury-prone movements can reach 300 milliseconds. Its early warning accuracy exceeds 93.5%, and the false alarm rate is below 3%, which is significantly related to subsequent abnormal postures (χ 2 test, p<0.001). SPOSE resolves the difficult problem of evaluating movement posture in high school physical education. Moreover, it reduces the risk of sports injuries and assists students in improving their athletic performance and safety. Thus, the intelligence and precision of physical education are promoted. The results demonstrate significant application potential for human kinematic analysis and motion injury risk identification.