IEEE Trans Neural Syst Rehabil Eng. 2021;29:2587-2594. doi: 10.1109/TNSRE.2021.3133616. Epub 2021 Dec 21.
Fall detection systems are designed in view to reduce the serious consequences of falls thanks to the early automatic detection that enables a timely medical intervention. The majority of the state-of-the-art fall detection systems are based on machine learning (ML). For training and performance evaluation, they use some datasets that are collected following predefined simulation protocols i.e. subjects are asked to perform different types of activities and to repeat them several times. Apart from the quality of simulating the activities, protocol-based data collection results in big differences between the distribution of the activities of daily living (ADLs) in these datasets in comparison with the actual distribution in real life. In this work, we first show the effects of this problem on the sensitivity of the ML algorithms and on the interpretability of the reported specificity. Then, we propose a reliable design of an ML-based fall detection system that aims at discriminating falls from the ambiguous ADLs. The latter are extracted from 400 days of recorded activities of older adults experiencing their daily life. The proposed system can be used in neck- and wrist-worn fall detectors. In addition, it is invariant to the rotation of the wearable device. The proposed system shows 100% of sensitivity while it generates an average of one false positive every 25 days for the neck-worn device and an average of one false positive every 3 days for the wrist-worn device.
跌倒检测系统旨在通过早期的自动检测来减少跌倒的严重后果，从而实现及时的医疗干预。大多数最先进的跌倒检测系统都是基于机器学习（ML）的。为了进行训练和性能评估，它们使用了一些数据集，这些数据集是根据预定义的模拟协议收集的，即要求受试者执行不同类型的活动，并多次重复这些活动。除了模拟活动的质量外，基于协议的数据收集导致这些数据集中日常活动（ADL）的分布与实际生活中的实际分布之间存在很大差异。在这项工作中，我们首先展示了这个问题对 ML 算法的灵敏度以及报告的特异性的可解释性的影响。然后，我们提出了一种基于可靠设计的 ML 跌倒检测系统，旨在区分跌倒和模糊的 ADL。后者是从 400 天记录的老年人日常生活活动中提取出来的。所提出的系统可用于颈戴和腕戴跌倒探测器。此外，它对可穿戴设备的旋转具有不变性。所提出的系统在颈戴设备中平均每 25 天产生一个假阳性，在腕戴设备中平均每 3 天产生一个假阳性，灵敏度达到 100%。
