Unsupervised Keypoints for Real-Time Fall Detection: Comparative Analysis Under Real-world Conditions with Predictive Bandwidth Reduction

תקציר מקורי באנגליתarXiv:2607.15400v3 Announce Type: replace-cross Abstract: Falls among older adults are a major safety and health-systems challenge, yet continuous in-person monitoring is difficult to sustain across home and clinical care settings. Video-based monitoring can capture fall-relevant motion, but scalable real-time deployment is limited by privacy, compute, and bandwidth constraints, and existing keypoint-based methods typically rely on supervised or anatomical pose representation, which is vulnerable to occlusion and partial body visibility. We propose a fall-monitoring framework that replaces continuous video transmission with compact motion representations, using unsupervised keypoints which are extracted locally, and a variational recurrent model is used to forecast motion at the staff end,