Common fall occurrences in the elderly population pose dramatic challenges in public healthcare domain. Adoption of an efficient and yet highly reliable automatic fall detection system may not only mitigate the adverse effects of falls through immediate medical assistance, but also profoundly improve the functional ability and confidence level of elder people. This paper presents a pervasive fall detection system developed on smart phones, namely, FallDroid that exploits a two-step algorithm proposed to monitor and detect fall events using the embedded accelerometer signals. Comprising of the threshold-based method and multiple kernel learning support vector machine, the proposed algorithm uses novel techniques to effectively identify fall-like events (such as lying on a bed or sudden stop after running) and reduce false alarms. In addition to user convenience and low power consumption, experimental results reveal that the system detects falls with high accuracy ($97.8\%$ and $91.7\%$), sensitivity ($99.5\%$ and $95.8\%$), and specificity ($95.2\%$ and $88.0\%$) when placed around the waist and thigh, respectively. The system also achieves the lowest false alarm rate of 1 alarm per 59 h of usage, which is best till date.

中文翻译：

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FallDroid：基于自动智能电话的跌倒检测系统，使用多核学习

在老年人口中常见的秋天事件在公共卫生保健领域提出了巨大的挑战。采用有效而又高度可靠​​的自动跌倒检测系统，不仅可以通过立即的医疗帮助减轻跌倒的不利影响，而且可以大大提高老年人的功能能力和信心水平。本文介绍了一种在智能手机上开发的无处不在的跌倒检测系统，即FallDroid，该系统利用建议的两步算法使用嵌入式加速度计信号监视和检测跌倒事件。该算法由基于阈值的方法和多核学习支持向量机组成，使用新颖的技术可以有效地识别类似跌倒的事件（例如躺在床上或跑步后突然停止）并减少错误警报。$ 97.8 \％$ 和 $ 91.7 \％$）， 灵敏度 （$ 99.5 \％$ 和 $ 95.8 \％$）和特异性（$ 95.2 \％$ 和 $ 88.0 \％$）分别放在腰部和大腿上。该系统还实现了每59小时使用1次警报的最低误报率，这是迄今为止最好的。