Name
Abstract | ||
|---|---|---|
Respiratory pattern tracking proved to be critical for many applications ranging from well-being monitoring and stress management to dealing with chronic breathing abnormalities. Specific breathing and meditation exercises have been designed to improve well-being of users based on monitoring the complete breathing waveform. While wearable systems had leveraged a wealth of information available from respiration stream in a variety of applications, contact-less sensing systems are lagging behind when it comes to capturing detailed breathing metrics. In this work we propose WiRelax; the first non-contact respiratory biofeedback system that relies solely on WiFi availability. We propose algorithms that map the changes in the Channel State Information (CSI) to the instantaneous breathing state. The key contribution is a model that relates relative phase of the received signal and the micro-motion of the chest during breathing. A novel processing pipeline is developed to extract a single breathing waveform from CSI data captured across noisy multiple sub-carriers in real-time. Our evaluation in a real-world setup shows that WiRelax can estimate real-time breath-by-breath cycle time with median error less than 0.25 s ( < 13% relative to the cycle length) thus enabling accurate device-free respiratory biofeedback. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.adhoc.2020.102226 | Ad Hoc Networks |
Keywords | DocType | Volume |
Biofeedback,Breathing,WiFi,Device-free | Journal | 107 |
ISSN | Citations | PageRank |
1570-8705 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdelwahed Khamis | 1 | 4 | 1.49 |
| Brano Kusy | 2 | 60 | 8.48 |
| Chun Tung Chou | 3 | 1197 | 90.98 |
| Wen Hu | 4 | 1373 | 104.78 |