Enhancing Performance and Reducing Injury Risk: Analyzing Basketball Players' Jumping Landing Form through Image Processing and Support Vector Machine Algorithm

Basketball involves frequent direction changes, quick acceleration, and big jumps, which increase the risk of landing-related injuries. Ankle sprains and knee injuries, such as ACL tears, are common among basketball players. Thus, we developed an analytical system to assess a basketball player's jump-landing performance with reduced risks of injuries. The system adopted an image processing algorithm, specifically Support Vector Machine and Image Processing techniques, to evaluate jumping landing techniques. The system was implemented using HTML, CSS, PHP, and JavaScript, providing real-time video feedback and evaluation reports. The accuracy of the system was validated through the expertise of a basketball coach. Data was collected using a Kinect sensor and a laptop webcam, and specialized software processed the data to extract relevant features. The system's performance was compared with assessments by coaches, and the collected data and analysis results were stored. The developed system accurately identified ideal jump landing techniques by assessing joint angles and key components of the landing. The analysis result showed strong correlations between evaluations and reference postures. The system can be used to reduce the risk of injuries and improve player performance in basketball. The study's findings provide promising implications for player safety and performance enhancement.