Luque Rafael, Casilari Eduardo, Morón María-José, Redondo Gema
Universidad de Málaga, Departamento de Tecnología Electrónica, ETSI Telecomunicación, 29071 Málaga, Spain.
Sensors (Basel). 2014 Oct 8;14(10):18543-74. doi: 10.3390/s141018543.
Falls are a foremost source of injuries and hospitalization for seniors. The adoption of automatic fall detection mechanisms can noticeably reduce the response time of the medical staff or caregivers when a fall takes place. Smartphones are being increasingly proposed as wearable, cost-effective and not-intrusive systems for fall detection. The exploitation of smartphones' potential (and in particular, the Android Operating System) can benefit from the wide implantation, the growing computational capabilities and the diversity of communication interfaces and embedded sensors of these personal devices. After revising the state-of-the-art on this matter, this study develops an experimental testbed to assess the performance of different fall detection algorithms that ground their decisions on the analysis of the inertial data registered by the accelerometer of the smartphone. Results obtained in a real testbed with diverse individuals indicate that the accuracy of the accelerometry-based techniques to identify the falls depends strongly on the fall pattern. The performed tests also show the difficulty to set detection acceleration thresholds that allow achieving a good trade-off between false negatives (falls that remain unnoticed) and false positives (conventional movements that are erroneously classified as falls). In any case, the study of the evolution of the battery drain reveals that the extra power consumption introduced by the Android monitoring applications cannot be neglected when evaluating the autonomy and even the viability of fall detection systems.
跌倒是老年人受伤和住院的首要原因。采用自动跌倒检测机制可以显著缩短跌倒发生时医护人员或护理人员的响应时间。越来越多的人提议将智能手机作为用于跌倒检测的可穿戴、经济高效且非侵入性的系统。利用智能手机的潜力（特别是安卓操作系统）可受益于这些个人设备的广泛普及、不断增强的计算能力以及通信接口和嵌入式传感器的多样性。在回顾了这方面的最新技术后，本研究开发了一个实验测试平台，以评估不同跌倒检测算法的性能，这些算法基于对智能手机加速度计记录的惯性数据的分析来做出决策。在针对不同个体的真实测试平台上获得的结果表明，基于加速度测量技术识别跌倒的准确性在很大程度上取决于跌倒模式。所进行的测试还表明，很难设置检测加速度阈值，以在漏报（未被注意到的跌倒）和误报（被错误分类为跌倒的常规动作）之间实现良好的权衡。无论如何，对电池电量消耗变化的研究表明，在评估跌倒检测系统的自主性乃至可行性时，安卓监测应用所带来的额外功耗不容忽视。
