Name
Abstract | ||
|---|---|---|
Human respiration state is an important indicator to reflect health conditions. Recent advances in wireless human sensing have enabled device-free respiration monitoring using narrow-band wireless signals, which, however, fail to map the estimated respiration states to multiple persons. In this paper, we present Multi-Breath, a UWB-based system to achieve separate respiration monitoring for multiple persons. The UWB radar can accurately measure the travelling distance of the signals, which helps to separate the signals affected by different persons and map the detected respiration patterns to the corresponding persons with the location information. However, the radar signal time series of each person are quite noisy due to the multi-path effects caused by the respiration movements of other persons, making it difficult to accurately estimate the respiration state. To overcome this challenge, we propose to transform the UWB radar signal matrices of different persons as separate RGB images to reveal the respiration pattern of each individual. Then, the image processing operations, including image smoothing, edge detection, dilation and erosion, are applied to identify the breathing cycles. Finally, the respiration state, including the respiration rate and the presence of apnea, is estimated via blob detection and calibration. Extensive experiments show that the mean absolute error on respiration rate estimation is 0.3 - 0.6 bpm, and the percentage of missed and false detected apnea is 3% - 7%. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/COMPSAC.2019.00124 | 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC) |
Keywords | DocType | Volume |
Respiration Rate Estimation,Apnea Detection,Multiple Persons,UWB Radar | Conference | 1 |
ISSN | ISBN | Citations |
0730-3157 | 978-1-7281-2607-4 | 0 |
PageRank | References | Authors |
0.34 | 15 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanni Yang | 1 | 50 | 6.49 |
| Jiannong Cao | 2 | 5226 | 425.12 |
| Xiulong Liu | 3 | 169 | 18.27 |
| Xuefeng Liu | 4 | 438 | 47.61 |