Falls present a significant global public health challenge, especially in today's aging society, underscoring the importance of developing an effective fall detection system. Non-invasive radio-frequency (RF) based fall detection has garnered substantial attention due to its wide coverage and privacy-preserving nature. Existing RF-based fall detection systems approach falls as an activity classification problem, assuming that human falls introduce reproducible patterns to the RF signals. However, we argue that falls are inherently accidental, making their impact uncontrollable and unforeseeable. We propose a fundamentally different approach to fall detection by shifting the focus from directly identifying hard-to-quantify falls to recognizing normal, repeatable human activities, thus treating falls as abnormal activities outside the normal activity distribution. We introduce a self-supervised incremental learning system incorporating FallNet, a deep neural network that employs unsupervised learning techniques. Our real-time fall detection system prototype leverages WiFi Channel State Information (CSI) sensing data and has been extensively tested with 16 human subjects.

翻译：跌倒是一项重大的全球公共卫生挑战，尤其在当今老龄化社会中尤为突出，这使得开发有效的跌倒检测系统具有重要意义。基于非侵入式射频（RF）的跌倒检测因其覆盖范围广、保护隐私等特点而受到广泛关注。现有的射频跌倒检测系统将跌倒视为活动分类问题，假设人类跌倒会对射频信号引入可复现的模式。然而，我们认为跌倒本质上具有偶然性，其影响不可控且不可预见。我们提出了一种根本不同的跌倒检测方法，将重点从直接识别难以量化的跌倒转向识别正常、可重复的人类活动，从而将跌倒视为正常活动分布之外的异常活动。我们引入了一种自监督增量学习系统，该系统包含FallNet——一种采用无监督学习技术的深度神经网络。我们的实时跌倒检测系统原型利用WiFi信道状态信息（CSI）传感数据，并已在16名人类受试者上进行了广泛测试。