AI - BASED DETECTION OF JOINT ISSUES FOR ELDERLY PEOPLE USING KNEE BRACES

Abstract

 This project presents an intelligent system designed to detect joint-related issues in elderly individuals using a smart knee brace integrated with MEMS-based motion sensors. As age progresses, joint flexibility, stability, and mobility decline, increasing the risk of pain, stiffness, and limited range of motion. Conventional clinical evaluation requires periodic hospital visits and lacks real-time monitoring, creating a need for a continuous and reliable assessment system. To address this gap, the proposed solution incorporates an accelerometer and gyroscope from a MEMS sensor within a wearable knee brace to measure movement patterns, angular displacement, and gait dynamics. These signals are processed to extract meaningful features such as joint flexion angles, tremor patterns, rotation irregularities, and motion asymmetry. A machine learning model is trained to classify joint flexibility levels and detect abnormalities associated with early degeneration or injury. The system continuously monitors knee movements during routine activities and provides data-driven insights into performance deviations. The predicted flexibility levels can assist physiotherapists and clinicians in early screening, recommending timely interventions, and tracking rehabilitation pr ogress. The proposed device is lightweight, cost-effective, and non-invasive, offering an efficient alternative for elderly users who require long-term mobility support. With real-time analytics and personalized feedback, the system has the potential to si gnificantly enhance preventive healthcare, reduce dependency, and improve the overall quality of life. This AI-driven knee brace framework provides a promising direction for creating smart wearable technologies aimed at detecting early joint issues with hi gh accuracy and reliability.