Abstract
This paper explores the influence of breathing rate variability on heart rate estimation through UWB radar sensing. The study leverages a low-power ultra-wideband radar system operating in the 7.29 to 8.748 GHz range with a 1.5 GHz bandwidth. Through meticulous data pre-processing and various deep learning models, the study classifies respiration rates into slow, normal, and fast categories. The results showcase the effectiveness of models such as MobileNet, ResNet50, and VGG19, achieving an impressive overall test accuracy of 93.3%. This research contributes to advancing the application of radar technology in the precise detection of vital signs, offering potential implications for non-invasive health monitoring.
| Original language | English |
|---|---|
| Pages (from-to) | 2799-2800 |
| Number of pages | 2 |
| Journal | AP-S International Symposium (Digest) (IEEE Antennas and Propagation Society) |
| DOIs | |
| State | Published - 2024 |
| Event | 2024 IEEE International Symposium on Antennas and Propagation and INC/USNCURSI Radio Science Meeting, AP-S/INC-USNC-URSI 2024 - Florence, Italy Duration: 14 Jul 2024 → 19 Jul 2024 |
Keywords
- UWB radar
- contactless sensing
- deep learning
- heart rate detection
- respiration rate detection
- vitals detection
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