Fall Detection and Boundary Detection in Care Homes

The elderly population represents a significant and rapidly expanding demographic, with a majority experiencing frequent daily accidents, notably falls. Falls rank as the second leading cause of accidental injury deaths globally. To address this issue, we propose a video classification system designed specifically for fall detection. Our fall detection framework comprises two key steps: firstly, the detection of human posture within video frames, followed by fall classification using Convolutional Neural Networks (CNNs). Additionally, we introduce a novel approach for boundary detection, utilizing object detection techniques beyond a predefined line of surveillance captured by a single camera. Through this integrated methodology, we aim to enhance fall detection and boundary breach detection capabilities, thereby contributing to the advancement of elderly care and safety. (Abstract).