发表机构
West Virginia University; Intel Corporation; Binghamton University（西弗吉尼亚大学; 英特尔公司; 宾汉姆顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究老年人跌倒检测问题，提出用基于无监督关键点和预测时间建模的隐私保护框架取代RGB传输，经多方式评估发现表示选择应依视觉条件，无监督关键点在身体可见性受限时有优势。
AI 中文摘要
老年人跌倒对安全构成重大挑战，持续监测困难。视频虽能捕捉跌倒相关姿势和动作，但受隐私、计算和带宽限制。有监督的姿势估计虽具解剖学可解释性，但易受遮挡和部分身体可见性影响。我们提出一个隐私保护框架，用基于无监督关键点和预测时间建模的紧凑运动表示取代RGB传输。通过局部处理进行分割和关键点提取，然后用变分循环预测和序列分类从观察到的和预测的运动中检测跌倒。我们在UR跌倒检测和人类跌倒数据集上使用随机、主体不相交和基于遮挡的分割方式评估该框架。结果表明，表示选择应反映预期视觉条件，在身体可见性受影响时，无监督关键点具有优势。
英文摘要
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, followed by fall classification. We evaluate the framework on the UR Fall and Human Fall datasets under random, subject-disjoint, and occlusion-based splits to systematically characterize when each representation has an advantage. We find that random splits do not discriminate between representations, and under subject-disjoint evaluation no uniform advantage emerges; performance varies across held-out subjects with their visual characteristics. Under occlusion, however, unsupervised keypoints substantially outperform supervised keypoints, retaining strong detection sensitivity where supervised keypoints miss approximately half of falls; this advantage reflects their anatomical independence and persists under bandwidth-constrained prediction. The unsupervised detector also requires roughly two orders of magnitude less computation, supporting privacy-preserving, bandwidth-aware, always-on fall monitoring.
