HUMAN FALL DETECTION ALGORITHM FOR THE ELDERLY BASED ON POSTURE ESTIMATION

Abstract

Falls among the elderly are a major health concern, often leading to severe injuries or complications. This project presents a Human Fall Detection Algorithm that utilizes computer vision and deep learning to accurately detect falls using human posture estimation. Unlike traditional sensor-based approaches, this system operates purely on video data, making it non intrusive and easy to deploy in real-world environments. The algorithm employs YOLO (You Only Look Once) for object detection and human posture analysis to classify various human states, distinguishing between normal activities and potential falls. OpenCV is used for real-time video processing, while PyTorch and TorchVision power the deep learning model execution. The system processes video frames, extracts key body landmarks, and determines fall events based on confidence scores and threshold values. This project aims to provide an accurate, efficient, and real-time solution for fall detection that can be integrated into assistive technologies, healthcare systems, or smart home monitoring solutions. The proposed approach ensures high precision in fall identification, making it a cost-effective and scalable alternative for elderly care.