A multimodal deep learning-based model for posture asymmetry recognition and sports injury risk prediction in adolescent table tennis athletes.

Background: Adolescent table tennis athletes face significant sports injury risks due to repetitive unilateral force generation patterns during the critical skeletal maturation period, yet traditional posture assessment methods lack quantitative precision and real-time monitoring capability. Methods: This study develops a multimodal deep learning framework integrating video RGB sequences, skeletal keypoint trajectories, and kinematic parameters through cross-modal attention mechanisms, weighted graph convolutional networks, and temporal convolutional networks to automatically recognize posture asymmetry patterns and assess biomechanical injury risk levels based on expert-evaluated postural deviation criteria, representing prospective biomechanical risk stratification for screening purposes rather than longitudinally validated injury occurrence prediction. Results: Comprehensive evaluation on the TTStroke-21 dataset demonstrates superior performance in both four-class posture asymmetry recognition and three-level injury risk prediction compared to baseline methods, validating the effectiveness of sport-specific architectural adaptations and multimodal data fusion strategies. The biomechanical analysis reveals quantitative relationships between technical movement patterns and asymmetry manifestations across different stroke types and age groups, confirming the critical intervention window during the 12-14-year developmental period. Conclusion: The proposed intelligent assessment system provides substantial practical value for training monitoring and injury prevention in youth sports, enabling coaches and sports medicine practitioners to implement data-driven personalized intervention strategies including contralateral limb strengthening programs and targeted corrective exercises before structural imbalances progress to clinical injury outcomes.