This research presents the development, implementation, and rigorous evaluation of an innovative Sports Injury Physiotherapist Detection Tool designed to revolutionize injury management in athletics. By integrating multi-modal real-time biomechanical data with advanced ensemble machine learning algorithms, this tool significantly enhances the accuracy and efficiency of sports injury diagnosis and prediction. Our study utilized a comprehensive dataset of 1,500 athletes across 15 diverse sports disciplines, collected over a three-year period, to assess the tool's performance.
Results demonstrate a remarkable improvement in diagnostic accuracy, with the tool achieving a 94.5% accuracy rate, 92.3% precision, and 93.1% recall in injury detection and prediction. These findings represent a substantial advancement over traditional assessment methods, which typically yield accuracy rates around 75-80%. The tool's capacity for early detection, often predicting injuries up to 14 days in advance with 87% accuracy, shows immense promise in minimizing athlete downtime and optimizing rehabilitation processes.