Efficient Fall Detection Technique Based on Markov Model: A Multi-Feature Analysis Approach
| dc.contributor.author | Gaur, Ankur Kumar | |
| dc.date.accessioned | 2026-09-22T07:16:35Z | |
| dc.date.available | 2026-09-22T07:16:35Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | 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. | |
| dc.identifier.uri | http://nits.ndl.gov.in/handle/123456789/112 | |
| dc.language.iso | en | |
| dc.publisher | National Institute of Technology, Silchar | |
| dc.title | Efficient Fall Detection Technique Based on Markov Model: A Multi-Feature Analysis Approach |
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