Departamento de Tecnología Electrónica, Universidad de Málaga, ETSI Telecomunicación, 29071 Málaga, Spain.
Sensors (Basel). 2021 Mar 23;21(6):2254. doi: 10.3390/s21062254.
Over the last few years, the use of smartwatches in automatic Fall Detection Systems (FDSs) has aroused great interest in the research of new wearable telemonitoring systems for the elderly. In contrast with other approaches to the problem of fall detection, smartwatch-based FDSs can benefit from the widespread acceptance, ergonomics, low cost, networking interfaces, and sensors that these devices provide. However, the scientific literature has shown that, due to the freedom of movement of the arms, the wrist is usually not the most appropriate position to unambiguously characterize the dynamics of the human body during falls, as many conventional activities of daily living that involve a vigorous motion of the hands may be easily misinterpreted as falls. As also stated by the literature, sensor-fusion and multi-point measurements are required to define a robust and reliable method for a wearable FDS. Thus, to avoid false alarms, it may be necessary to combine the analysis of the signals captured by the smartwatch with those collected by some other low-power sensor placed at a point closer to the body's center of gravity (e.g., on the waist). Under this architecture of Body Area Network (BAN), these external sensing nodes must be wirelessly connected to the smartwatch to transmit their measurements. Nonetheless, the deployment of this networking solution, in which the smartwatch is in charge of processing the sensed data and generating the alarm in case of detecting a fall, may severely impact on the performance of the wearable. Unlike many other works (which often neglect the operational aspects of real fall detectors), this paper analyzes the actual feasibility of putting into effect a BAN intended for fall detection on present commercial smartwatches. In particular, the study is focused on evaluating the reduction of the battery life may cause in the watch that works as the core of the BAN. To this end, we thoroughly assess the energy drain in a prototype of an FDS consisting of a smartwatch and several external Bluetooth-enabled sensing units. In order to identify those scenarios in which the use of the smartwatch could be viable from a practical point of view, the testbed is studied with diverse commercial devices and under different configurations of those elements that may significantly hamper the battery lifetime.
在过去的几年中,智能手表在自动跌倒检测系统(FDS)中的应用引起了人们对新的可穿戴远程监测老年人系统研究的极大兴趣。与其他跌倒检测方法相比,基于智能手表的 FDS 可以受益于这些设备广泛的接受度、人体工程学、低成本、网络接口和传感器。然而,科学文献表明,由于手臂的自由运动,手腕通常不是明确描述跌倒时人体动力学的最合适位置,因为许多涉及手部剧烈运动的日常活动可能很容易被误解为跌倒。正如文献所述,需要传感器融合和多点测量来定义一种稳健可靠的可穿戴 FDS 方法。因此,为了避免误报,可能需要将智能手表捕捉到的信号分析与那些放置在更接近身体重心的其他低功耗传感器(例如腰部)收集到的信号进行结合。在这种身体区域网络(BAN)架构下,这些外部感应节点必须通过无线方式连接到智能手表,以传输其测量结果。尽管如此,这种网络解决方案的部署,其中智能手表负责处理感测数据并在检测到跌倒时生成警报,可能会严重影响可穿戴设备的性能。与许多其他作品(它们经常忽略实际跌倒探测器的操作方面)不同,本文分析了在当前商业智能手表上实施用于跌倒检测的 BAN 的实际可行性。特别是,该研究侧重于评估作为 BAN 核心的手表的电池寿命可能会减少。为此,我们彻底评估了由智能手表和几个外部蓝牙启用感应单元组成的 FDS 原型的能耗。为了确定从实际角度来看使用智能手表是否可行的那些场景,该测试平台使用各种商业设备进行了研究,并针对可能显著缩短电池寿命的那些元素的不同配置进行了研究。