AI-POWERED GAIT ANALYSIS FOR EARLY PARKINSON’S DETECTION

Abstract

Parkinson’s disease (PD) often manifests through gait abnormalities that can appear before a formal diagnosis. This project presents a unique wearable gait analysis system designed for early detection of PD using advanced sensor technology and AI. The system integrates an inertial measurement unit (IMU) to monitor key gait parameters such as stride length, walking speed, tremor intensity, and balance, alongside pressure sensors to assess foot pressure distribution, providing a comprehensive gait profile. Its distinctive feature includes real-time data visualization on a mobile app, which alerts users to abnormal gait patterns. The system incorporates adaptive AI that improves detection accuracy with continuous data collection. Lightweight and portable, it is designed for home use, offering an affordable and non-invasive alternative to clinical assessments. By enabling continuous monitoring and early detection, this system supports timely medical intervention, improving long-term outcomes and providing a proactive Parkinson’s disease. approach to managing