Falls are one of the leading causes of death in the elderly people aged 65 and above. In order to prevent death by sending prompt fall detection alarms, non-invasive radio-frequency (RF) based fall detection has attracted significant attention, due to its wide coverage and privacy preserving nature. Existing RF-based fall detection systems process fall as an activity classification problem and assume that human falls introduce reproducible patterns to the RF signals. We, however, argue that the fall is essentially an accident, hence, its impact is uncontrollable and unforeseeable. We propose to solve the fall detection problem in a fundamentally different manner. Instead of directly identifying the human falls which are difficult to quantify, we recognize the normal repeatable human activities and then identify the fall as abnormal activities out of the normal activity distribution. We implement our idea and build a prototype based on commercial Wi-Fi. We conduct extensive experiments with 16 human subjects. The experiment results show that our system can achieve high fall detection accuracy and adapt to different environments for real-time fall detection.

翻译：跌倒是65岁及以上老年人死亡的主要原因之一。为了通过发送即时跌落探测警报来预防死亡,非侵入性无线电频率的跌落探测因其覆盖面广泛和隐私保护性质而引起人们的极大关注。现有的以俄罗斯联邦为基础的跌落探测系统过程作为一个活动分类问题而掉落,并假定人类坠落会给RF信号带来可复制的模式。然而,我们争辩说,跌落基本上是一场事故,因此,其影响是不可控制的和无法预见的。我们提议以根本不同的方式解决跌落探测问题。我们不直接确定难以量化的人类跌落,而是确认正常的重复人类活动,然后将跌落确定为正常活动分布的异常活动。我们实施我们的想法,在商业Wi-Fi的基础上建立一个原型。我们用16个人类实验对象进行广泛的实验。实验结果表明,我们的系统可以实现高跌落探测准确度,并适应不同的环境进行实时跌落探测。