CONSTRUCTION OF THE ATHLETIC ABILITY ASSESSMENT SYSTEM IN SPORTS TEACHING INTEGRATING ARTIFICIAL INTELLIGENCE AND CONVOLUTIONAL NEURAL NETWORK MODELS

Abstract

In a college physical education teaching, college students’ athletic ability assessment still faces problems of strong subjectivity in scoring and difficulty in quantifying complex movement characteristics. Traditional methods struggle to capture multi-granularity information of skeletons in continuous movements, and the cost of acquiring annotated data is high. This study aims to construct a set of quantitative assessment models for athletic ability oriented to college physical education classrooms. It realizes the joint optimization of action recognition and athletic ability scoring through the multi-granularity spatial-temporal graph convolutional network (MG-STGC). The MG-STGC model uses an encoder to extract joint-level, limb-level, and body-level features. It combines labeled and unlabeled data via semi-supervised learning strategies to achieve the joint optimization of action recognition and quantitative assessment of athletic ability. The athletic ability assessment module can generate continuous scores across four dimensions: strength, stability, standardization, and coordination. These scores are obtained through spatiotemporal statistical mapping of historical action segments and skeleton features, providing data support for individualized training. On the NTU RGB+D dataset, MG-STGC achieves a Top-1 accuracy of 95.6% and 89.7% on the X-view and X-sub benchmarks. On the FineGym dataset, it reaches 80.5% Top-1 accuracy on the Gym99 subset and 75.4% on the Gym288 subset, with category average accuracies of 69.8% and 62.6%, respectively, outperforming baseline models. Ablation experiments show that the Granularity Information Fusion Module (GIFM) and parameters have an impact on model performance. The research shows that MG-STGC can efficiently capture multi-granularity information of action skeletons and provide an objective and quantitative method for athletic ability assessment in college physical education classrooms. MG-STGC also lays a feasible theoretical and practical foundation for intelligent physical education teaching and personalized training.