Vision Based Deep Learning Fall Detection System for Elderly People

This paper presents a vision-based fall detection system designed to enhance the safety and well-being of elderly individuals through real-time monitoring and rapid emergency response. Falls among the elderly represent a major global health concern, often leading to serious injuries, long-term disabilities, and reduced quality of life, particularly for individuals living alone. Conventional fall detection methods, such as wearable sensors and manual supervision, are limited by user discomfort, high costs, and a high rate of false alarms. To address these challenges, the proposed system integrates the YOLO object detection algorithm with Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to accurately detect fall events from continuous video streams. The video data undergo pre-processing steps including frame extraction, normalization, and background noise reduction. YOLO is employed to detect and localize the human subject in each frame, while the CNN-LSTM architecture learns discriminative spatiotemporal features corresponding to fall-related motion patterns, such as abrupt posture changes, rapid downward movements, and abnormal body orientations. The system is trained and evaluated using annotated fall and non-fall video sequences, achieving high accuracy, sensitivity, and specificity while maintaining real-time performance. Furthermore, the proposed approach is robust to variations in lighting conditions, environments, and subject appearances. As a non-intrusive and cost-effective solution, the system is well suited for deployment in smart homes, intelligent healthcare environments, and assistive living applications. The results demonstrate the potential of the proposed method to enable timely medical intervention and significantly improve the quality of life for elderly individuals.