Smart physical activity monitoring for preventing shoulder impingement syndrome

Shoulder impingement syndrome (SIS) is a prevalent musculoskeletal condition requiring effective preventive strategies. This study introduces a comprehensive approach to SIS prevention through smart physical activity monitoring. Wearable sensors, including gyroscopes and accelerometers, we reutilized to gather detailed motion data during various physical activities. A total of 50 participants for each group engaged in repetitive overhead tasks associated with SIS development. Sensor data analysis revealed increased deviation from normal shoulder movement during overhead tasks, particularly among participants with SIS. Machine learning models trained on this data exhibited promising results in predicting impingement risk, achieving 85–88% accuracy. Real-time feedback mechanisms were designed to provide proactive guidance during physical activities, contributing to effective impingement risk mitigation. The findings underscore the potential of smart physical activity monitoring in SIS prevention and musculoskeletal health promotion. Real-time feedback mechanisms offer personalized guidance, enhancing intervention strategies. Validation and verification procedures ensure the reliability and validity of the proposed monitoring system. This research signifies a significant advancement in SIS prevention, highlighting the role of smart technology in promoting musculoskeletal health. Future recommendations include the integration of advanced machine learning algorithms and continuously refining feedback mechanisms to optimize preventive interventions.