A Vision-based Gait Analysis and Fall Detection Using YOLOv11 Model
V S Rishith Reddy, Karthikeyan Srinivas Chintala, M Amsaprabhaa, S. Vidhusha · 2025
Falls present a significant health risk, particularly for the elderly, often stemming from impaired coordination and gait abnormalities. Early detection of falls is crucial for preventing injuries and ensuring timely intervention. This paper introduces a vision-based framework for fall detection that utilizes spatiotemporal gait features extracted from video footage. This system continuously monitors and analyses gait patterns by employing advanced computer vision techniques to identify fall risks. The proposed system integrates technologies to enhance real-time data processing, thereby improving fall detection accuracy. Experimental results demonstrate the system’s effectiveness, underscoring its potential for widespread application in healthcare settings to protect individuals at risk of falling.