Wearable Sensor Technology for Real-Time Biomechanical Analysis in Competitive Sports

The wearable sensor technology is able to give real-time data on the patterns of movement, strain on physiological levels, in addition to a measure of injury risk whereas offering a viable alternative to the costly motion capture systems which depend on the analysis of the post-test data. The paper will suggest an AI-enhanced biomechanical system, which includes IMUs, EMG sensors, pressure sensors, GPS, and heart-rate sensor to track the performance of an athlete, identify inefficient movements, and predict injury. The combination of machine learning and cloud processing allows providing immediate feedback and custom training suggestions. The architecture integrates wireless transfer of data, AI-driven identification of patterns and real-time visualization to the athletes and coaches. The experimental findings have shown that there is an increase in movement efficiency and decreased risk of injury, which makes this valid and scalable substitute to mainstream biomechanical instruments less expensive. Future enhancement will involve augmented reality, better predictive analytics, and minimization of sensors.