Abstract
In this article, we use WIFI signals to estimate the human respiratory rate. We propose a method for estimating static human respiration rate using CSI signals. We perform continuous wavelet transform on the CSI signal to obtain a time-frequency graph, and propose the Breath-net to learn the features related to respiration in the time-frequency graph. Finally, the Breath-net estimates the respiration rate. The results show that the average absolute error of this method for predicting respiratory rate can reach approximately 0.2 bpm.
| Original language | English |
|---|---|
| Pages (from-to) | 3814-3819 |
| Number of pages | 6 |
| Journal | IET Conference Proceedings |
| Volume | 2023 |
| Issue number | 47 |
| DOIs | |
| State | Published - 2023 |
| Externally published | Yes |
| Event | IET International Radar Conference 2023, IRC 2023 - Chongqing, China Duration: 3 Dec 2023 → 5 Dec 2023 |
Keywords
- Channel state information
- commodity Wi-Fi
- deep learning
- respiration estimation
- time-frequency analysis