Elderly Fall Detection Using Deep Learning Techniques

Abstract

Elderly Fall Detection Using Deep Learning Techniques is an intelligent healthcare monitoring system designed to automatically detect fall incidents in real time. Falls are one of the leading causes of serious injuries among older adults, often resulting in fractures, hospitalization, or life-threatening complications. The proposed system utilizes deep learning models to analyze video input and identify abnormal human postures and sudden movements associated with falls. Convolutional Neural Networks (CNN) and temporal learning models are employed to extract spatial and motion-based features from video frames. The system distinguishes between normal daily activities such as walking, sitting, or bending and actual fall events with high accuracy. When a fall is detected, the system immediately generates an alert notification to caregivers or family members, ensuring quick assistance. This approach enhances elderly safety, reduces response time in emergencies, and supports independent living through continuous and automated monitoring.