AI-Driven Fall Detection Framework for Assisted Living Environments

Abstract

This paper presents an AI-driven fall
detection framework designed to improve
safety in assisted living environments.
The system processes video streams and
applies preprocessing techniques to
enhance input quality. Blob detection is
used to identify human movement regions
from video frames. Pre-trained deep
learning models are integrated with
RCNN and YOLO algorithms for
accurate activity detection. The
framework classifies activities and
identifies fall events in real time. When a
fall is detected, an alert is generated
immediately for caregivers or medical
staff. The proposed approach reduces
dependency on manual monitoring
systems. It enhances detection accuracy
by combining computer vision and deep
learning methods. The system is suitable
for continuous surveillance in indoor
environments. This framework aims to
support elderly care and prevent