Real-Time Fall Detection in Clinical and HomeEnvironments Using YOLO-Based PoseEstimation and Spatio-Temporal Skeletal Features

Falls have continued to pose a significant risk, particularly for the elderly. Preventing injuries and fatalities has required accurate and timely detection. However, the complexity of real-world environments and the need for precision have presented ongoing challenges to existing fall detection systems. While wear-able sensors have proven useful, they are often uncomfortable for continuous use, and traditional detection methods have demonstrated unreliability due to their sensitivity to environmental conditions. Consequently, the development of a more accurate, real-time, non-invasive, and environment-independent detection 1 approach has become essential. In this study, we have developed and evaluated two novel vision-based fall detection systems. In the first system, we have employed You Only Look Once , version 8 (YOLOv8) or YOLOv11 for real-time detection of both the person and the bed within each video frame. Subsequently, we have applied AlphaPose to extract human body keypoints, followed by action recognition using Spatial-Temporal Graph Convolutional Networks (ST-GCN). A custom fall detection logic has been integrated, which evaluates both posture and spatial position relative to the bed to confirm fall events. In the second system , we have utilized pose-based models (YOLOv8-pose or YOLOv11-pose) that simultaneously detect the person and estimate keypoints. Based on this data, we have designed an independent fall logic that classifies fall events through posture and location analysis. This system has also incorporated a real-time alert mechanism that sends WhatsApp notifications to enable immediate response in the event of a fall. Experimental results have demonstrated that both systems offer robust and reliable fall detection across various scenarios, significantly enhancing safety and supporting the well-being of individuals at risk.