Abstract
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 (k-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.
Highlights
According to demographic prognosis, it is evident that the ratio between the number of groups of people who need different kinds of care services, such as the elderly and minors, and those of working-age, is increasing globally [1]
We propose a solution which is very simple, friendly for the user, and easy to implement in the home, while still providing good accuracy
Single UWB radar used to identify dynamic and static postures of the user in a room; Investigation of the possibility to extract breathing and coughing rates when the user is in a static posture; Selection of three simple statistical parameters to discriminate between static postures; machine learning (ML) technique, called as k-nearest neighbour, to automatically classify static postures; Reliability and fault tolerance analysis for the radar device and a home network; Network architecture solutions proposed to support such home networks in a largescale deployment
Summary
It is evident that the ratio between the number of groups of people who need different kinds of care services, such as the elderly and minors, and those of working-age, is increasing globally [1]. Mains-connected devices do not require battery charging, which simplifies their usage This is especially important in cases when remotely monitoring an elderly person’s activities or some of their vitals in their normal living environment is required. One field where UWB radar can be used in the healthcare domain is, for example, contactless respiratory and heartrate detection, which requires a good time domain resolution This information can be squeezed out from the detected UWB radar signal, as the very wide bandwidth enables high resolution to distinguish tiny body movements. Using only one UWB device, it is possible to detect a person(s) presence, movement, and some of their vital signs, all at the same time The latter option typically requires a static environment and a rather short distance between the UWB radar and its object, due to the small power levels used. The use of UWB radar technology does not need a line-of-sight view either, which improves its usability for through-the-wall type measurements
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