Mokhtari G, Aminikhanghahi S, Zhang Q, Cook D J
Australian e-Health Research Centre, CSIRO, Brisbane, Australia.
Washington State University, Pullman, USA.
J Reliab Intell Environ. 2018;4:131-139. doi: 10.1007/s40860-018-0065-2. Epub 2018 Jul 31.
Falls are one of the major issues which can endanger lives for older adults. Numerous research studies investigate the use of wearable technologies to detect falls in everyday environments. Although wearable sensor solutions provide good accuracy and sensitivity for fall detection, it may not always be convenient or desirable for older adults to wear a tag or sensor in home environments. This paper discusses using non-wearable UWB radar sensors as a practical, environmental fall detection solution in home settings. Specifically, we apply unsupervised change detection methods on UWB sensor data to detect falls. Furthermore, to evaluate the generality of our unsupervised approach, we also apply it to fall detections from accelerometer sensor data. The proposed methods are assessed using the real UWB sensor data sets acquired from the Living Lab at Australian e-Health Research Centre and public available accelerometer sensor data sets. The results show promising outcomes.
跌倒问题是危及老年人生命的主要问题之一。许多研究探讨了可穿戴技术在日常环境中用于检测跌倒的情况。尽管可穿戴传感器解决方案在跌倒检测方面具有良好的准确性和灵敏度，但在家庭环境中，老年人佩戴标签或传感器可能并不总是方便或理想的。本文讨论了使用非可穿戴超宽带（UWB）雷达传感器作为家庭环境中实用的环境跌倒检测解决方案。具体而言，我们将无监督变化检测方法应用于UWB传感器数据以检测跌倒。此外，为了评估我们无监督方法的通用性，我们还将其应用于加速度计传感器数据的跌倒检测。所提出的方法使用从澳大利亚电子健康研究中心的生活实验室获取的真实UWB传感器数据集以及公开可用的加速度计传感器数据集进行评估。结果显示出了有前景的成果。
