A Vision Transformer-Based Multi-Camera Biomechanical Motion Analysis Framework for Early Sports Injury Risk Prediction in Elite Athletes

Abstract

Sports injuries remain one of the most significant challenges affecting elite athletic performance, career longevity, and competitive success across professional sports. High-intensity training, repetitive biomechanical loading, improper movement mechanics, muscular imbalances, fatigue accumulation, and inadequate recovery substantially increase the probability of musculoskeletal injuries involving the anterior cruciate ligament (ACL), hamstrings, Achilles tendon, shoulder complex, and lower back. Traditional injury prevention strategies primarily depend on manual video analysis, wearable sensors, force plate assessments, and expert clinical evaluation. Although these approaches provide valuable biomechanical insights, they often require specialized laboratory equipment, extensive human expertise, time-consuming analysis, and subjective interpretation, limiting their scalability for continuous athlete monitoring during routine training sessions. Recent advances in computer vision, deep learning, and artificial intelligence provide new opportunities for automated markerless biomechanical analysis capable of continuously assessing movement quality and identifying injury risks before clinical symptoms appear. This paper proposes a Vision Transformer-Based Multi-Camera Biomechanical Motion Analysis Framework for early sports injury risk prediction in elite athletes. The proposed architecture integrates synchronized multi-camera video acquisition, markerless human pose estimation, three-dimensional skeletal reconstruction, Vision Transformer-based spatiotemporal feature extraction, biomechanical parameter analysis, temporal motion modeling, explainable artificial intelligence, and injury risk prediction into a unified intelligent framework. Multi-view synchronized cameras continuously capture athlete movements during sprinting, jumping, cutting, landing, throwing, and change-of-direction activities. Markerless pose estimation reconstructs accurate three-dimensional skeletal trajectories, while Vision Transformers learn global biomechanical relationships among body joints across temporal sequences. The framework further estimates joint angles, angular velocity, acceleration, ground contact characteristics, symmetry indices, kinetic chain coordination, and fatigue-related movement deviations to identify early biomechanical abnormalities associated with sports injuries. Experimental evaluation demonstrates that the proposed framework significantly improves injury prediction accuracy, movement classification performance, biomechanical interpretation, computational robustness, and early injury detection compared with conventional CNN-based motion analysis systems. The proposed architecture establishes an intelligent and scalable foundation for next-generation AI-assisted athlete monitoring and precision sports medicine.