Gaur, Ankur Kumar2026-09-222026-09-222026http://nits.ndl.gov.in/handle/123456789/112Fall 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.en