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：

---

基于智能手机的跌倒检测在线系统，具有警报通知和现实跌倒的上下文信息


跌倒是全世界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。在实验室条件下模拟跌倒的回顾性调查和研究经常被使用并且信息丰富，但有关现实生活中跌倒的前瞻性信息仍然很少。此类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，该研究调查了基于智能手机的跌倒检测和通知在线系统的概念验证。该系统使用智能手机的加速计和陀螺仪来监控参与者的运动，并使用正则化逻辑回归来检测跌倒。有关跌倒和接近跌倒事件（即绊倒）的数据存储在云服务器中，与跌倒相关的变量记录在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的位置和位置。秋季前的活动。总共有 23 名跌倒风险较高的人佩戴手机 2070 天，模型对 14,904,000 起事件进行了分类。系统检测到发生的 37 次跌倒中的 27 次（灵敏度 = 73.0 %），并每 46 天产生一次误报（特异性 > 99.9 %，精确度 = 37.5 %）。 42.2% 被错误归类为跌倒的事件被验证为绊倒。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。除了作为实用的跌倒监测仪器外，该系统还可以作为一种有价值的研究工具，使未来的研究能够扩大捕获跌倒相关数据的能力，并帮助研究人员和临床医生调查真实的跌倒情况。