GaitSynergy: Redefining Gait Analysis with Vision-EMG Convergence for Precise Neuro-Muscular Evaluation

Quantitative analysis of gait has significantly contributed to our understanding of the intricate dynamics of human locomotion. Leveraging the integration of computer vision and electromyography (EMG) signals, our study seeks to enhance the understanding of the gait cycle dynamics in real-time. The project involves the simultaneous capture of gait videos through computer vision techniques and the monitoring of muscle activities using EMG signals. By synergizing these two methodologies, we aim to create an efficient and affordable tool for improved patient care and diagnostics. The proposed model incorporates AI-driven computer vision, specifically utilizing the Mediapipe library, to calculate essential gait parameters. Additionally, the integration of a budget-friendly EMG sensor, AD8226, making this gait assessment model more accessible. A user-friendly web app with authentication features is also being developed to facilitate seamless access for trained professionals and ensure data security. This innovative approach not only addresses the current challenges in healthcare accessibility for neuromuscular disorders in India but also offers a comprehensive and affordable solution for gait analysis.