Integrating Computer Vision and Biomechanism for Injury Prevention for Cricket Bowlers

Cricket bowlers are prone to injuries due to repetitive motion and biomechanical stress, which can affect their career and team performance. This research proposes a new framework that integrates biomechanics and computer vision techniques for early prevention of injuries in cricket bowlers. By analyzing movement patterns and detecting biomechanical abnormalities, the system provides data-driven insights for injury risk assessment and performance improvement. The study uses advanced computer vision algorithms to extract 3D kinetic data from high-speed video footage of bowlers in real-time. Train on machine learning models, biomechanical datasets and injury history. Machine learning models, trained on biomechanical datasets and injury history, are used to identify stress points and deviations from optimal bowling techniques. Validation results demonstrate that the proposed system achieves greater than 85% accuracy in identifying high-risk movement patterns, outperforming traditional biomechanical analysis methods. This approach eliminates the need for complex marker-based motion capture systems, making it accessible and scalable for real-world applications. The integration of interpretable AI ensures actionable recommendations for coaches and medical teams, enabling personalized training interventions and reducing injury rates