Name
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/s21093158 | SENSORS |
Keywords | DocType | Volume |
home, living, movement identification, remote monitoring, signal classification, k-nearest neighbour | Journal | 21 |
Issue | ISSN | Citations |
9 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matti Hämäläinen | 1 | 0 | 0.34 |
| Lorenzo Mucchi | 2 | 235 | 38.89 |
| Stefano Caputo | 3 | 1 | 3.05 |
| Lorenzo Biotti | 4 | 0 | 0.68 |
| Lorenzo Ciani | 5 | 0 | 2.37 |
| Dania Marabissi | 6 | 0 | 0.68 |
| Gabriele Patrizi | 7 | 5 | 6.01 |