Name
Abstract | ||
|---|---|---|
Wireless detection of respiration rate (RR) plays a significant role in many healthcare applications. Most of the solutions provide simple waveforms and display the number of estimated RR. In this paper, a visualiser's approach for the wireless respiration tracking with commercial radar sensor, Walabot, is proposed. Walabot provides a real-time 2D heatmap from the reflected signals, and an abstract graph is extracted from the heatmap to represent the respiratory motion intuitively. Since monitored objects' movements may cause inaccurate measurements, two optimisation algorithms are developed to enhance accuracy. A respiration waveform is then obtained, and the average RR is calculated. The data is collected for different scenarios of breathing speed, including normal, hold, and deep. The computed RR accuracy is compared with the manually counted RR during the data collection as a reference. Overall, the calculated RR has high average accuracy, and the performance of the visualiser is precise and consistent. This solution also has the potential to monitor respirations for multiple subjects or in a through-wall situation. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-95593-9_13 | BODY AREA NETWORKS: SMART IOT AND BIG DATA FOR INTELLIGENT HEALTH MANAGEMENT |
Keywords | DocType | Volume |
Data visualization, Wireless monitoring, Respiration rate | Conference | 420 |
ISSN | Citations | PageRank |
1867-8211 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shaozhang Dai | 1 | 0 | 0.34 |
| Weiqiao Han | 2 | 0 | 0.34 |
| Malikeh Pour Ebrahim | 3 | 2 | 2.76 |
| Mehmet R. Yuce | 4 | 0 | 0.34 |