(National Institute of Technology, Silchar, 2026) Gaur, Ankur Kumar
Fall detection plays an important role in healthcare monitoring, particularly for elderly patients and individuals because it triggers an alert and allows for faster interventions when a patient experiences a fall. This work presents a lightweight, vision-based fall detection framework designed for real-time deployment in monitoring environments. We propose a Hidden Markov Model-based approach that leverages YOLOv8 pose es-timation to detect fall events from video streams. Our method extracts multi-feature from human pose key point, including head position, body angle, center of mass, ve-locity etc. These features capture various fall patterns. The HMM model providing robust detection and achieve competitive accuracy while meeting real-time processing requirements, making it suitable for continuous monitoring applications.