Falls are a leading cause of accidental deaths and injuries worldwide. The risk of falling is especially high for individuals suffering from balance impairments. Retrospective surveys and studies of simulated falling in lab conditions are frequently used and are informative, but prospective information about real-life falls remains sparse. Such data are essential to address fall risks and develop fall detection and alert systems. Here we present the results of a prospective study investigating a proof-of-concept, smartphone-based, online system for fall detection and notification. The system uses the smartphone’s accelerometer and gyroscope to monitor the participants’ motion, and falls are detected using a regularized logistic regression. Data on falls and near-fall events (i.e., stumbles) is stored in a cloud server and fall-related variables are logged onto a web portal developed for data exploration, including the event time and weather, fall probability, and the faller’s location and activity before the fall. In total, 23 individuals with an elevated risk of falling carried the phones for 2070 days in which the model classified 14,904,000 events. The system detected 27 of the 37 falls that occurred (sensitivity = 73.0 %) and resulted in one false alarm every 46 days (specificity > 99.9 %, precision = 37.5 %). 42.2 % of the events falsely classified as falls were validated as stumbles. The system’s performance shows the potential of using smartphones for fall detection and notification in real-life. Apart from functioning as a practical fall monitoring instrument, this system may serve as a valuable research tool, enable future studies to scale their ability to capture fall-related data, and help researchers and clinicians to investigate real-falls.

中文翻译：

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基于智能手机的跌倒检测在线系统，带有警报通知和现实跌倒的上下文信息

跌倒是世界范围内意外死亡和受伤的主要原因。对于患有平衡障碍的人来说，跌倒的风险尤其高。经常使用在实验室条件下模拟跌倒的回顾性调查和研究，并且提供信息，但关于现实生活中跌倒的前瞻性信息仍然很少。这些数据对于解决跌倒风险和开发跌倒检测和警报系统至关重要。在这里，我们展示了一项前瞻性研究的结果，该研究调查了用于跌倒检测和通知的基于智能手机的概念验证在线系统。该系统使用智能手机的加速度计和陀螺仪来监控参与者的运动，并使用正则化逻辑回归检测跌倒。跌倒和接近跌倒事件的数据（即，stumbles) 存储在云服务器中，与跌倒相关的变量被记录到为数据探索而开发的门户网站上，包括事件时间和天气、跌倒概率以及跌倒者在跌倒前的位置和活动。总共有 23 名跌倒风险较高的人携带手机 2070 天，其中模型对 14,904,000 个事件进行了分类。系统检测到发生的 37 次跌倒中的 27 次（灵敏度 = 73.0 %），并导致每 46 天出现一次误报（特异性 > 99.9 %，精确度 = 37.5 %）。42.2% 被错误归类为跌倒的事件被证实为跌倒。该系统的性能显示了在现实生活中使用智能手机进行跌倒检测和通知的潜力。除了用作实用的跌倒监测仪器外，该系统还可用作有价值的研究工具，