Centre for Wireless Communications, University of Oulu, 90570 Oulu, Finland.
Department of Information Engineering, University of Florence, I-50139 Firenze, Italy.
Sensors (Basel). 2021 May 2;21(9):3158. doi: 10.3390/s21093158.
In this paper, we propose an unobtrusive method and architecture for monitoring a person's presence and collecting his/her health-related parameters simultaneously in a home environment. The system is based on using a single ultra-wideband (UWB) impulse-radar as a sensing device. Using UWB radars, we aim to recognize a person and some preselected movements without camera-type monitoring. Via the experimental work, we have also demonstrated that, by using a UWB signal, it is possible to detect small chest movements remotely to recognize coughing, for example. In addition, based on statistical data analysis, a person's posture in a room can be recognized in a steady situation. In addition, we implemented a machine learning technique (-nearest neighbour) to automatically classify a static posture using UWB radar data. Skewness, kurtosis and received power are used in posture classification during the postprocessing. The classification accuracy achieved is more than 99%. In this paper, we also present reliability and fault tolerance analyses for three kinds of UWB radar network architectures to point out the weakest item in the installation. This information is highly important in the system's implementation.
在本文中，我们提出了一种在家庭环境中同时监测人员存在并收集其与健康相关参数的非侵入性方法和体系结构。该系统基于使用单个超宽带 (UWB) 脉冲雷达作为感测设备。我们旨在使用 UWB 雷达来识别人员和一些预选动作，而无需使用相机类型的监控。通过实验工作，我们还证明，通过使用 UWB 信号，可以远程检测到胸部的微小运动，例如检测到咳嗽。此外，基于统计数据分析，可以在稳定状态下识别房间内人员的姿势。此外，我们还实现了一种机器学习技术（-最近邻），以使用 UWB 雷达数据自动对静态姿势进行分类。在进行姿态分类时，偏度、峰度和接收功率在姿态分类中都有使用。所达到的分类准确率超过 99%。在本文中，我们还针对三种 UWB 雷达网络架构进行了可靠性和容错性分析，以指出安装中的薄弱环节。这些信息在系统实施中非常重要。
