Badshah, S. S., Saeed, U., Momand, A., Shah, S. Y., Shah, S. I., Ahmad, J., Abbasi, Q. H. and Shah, S. A. (2022) UWB Radar Sensing for Respiratory Monitoring Exploiting Time-Frequency Spectrograms. In: 2nd International Conference of Smart Systems and Emerging Technologies (SMARTTECH 2022), Riyadh, Saudi Arabia, 22-24 May 2022, pp. 136-141. ISBN 9781665409735 (doi: 10.1109/SMARTTECH54121.2022.00040)
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Abstract
Regarding the health-related applications in infectious respiratory/breathing diseases including COVID-19, wireless (or non-invasive) technology plays a vital role in the monitoring of breathing abnormalities. Wireless techniques are particularly important during the COVID-19 pandemic since they require the minimum level of interaction between infected individuals and medical staff. Based on recent medical research studies, COVID-19 infected individuals with the novel COVID-19-Delta variant went through rapid respiratory rate due to widespread disease in the lungs. These unpleasant circumstances necessitate instantaneous monitoring of respiratory patterns. The XeThru X4M200 ultra-wideband radar sensor is used in this study to extract vital breathing patterns. This radar sensor functions in the high and low-frequency ranges (6.0-8.5 GHz and 7.25-10.20 GHz). By performing eupnea (regular/normal) and tachypnea (irregular/rapid) breathing patterns, the data were acquired from healthy subjects in the form of spectrograms. A cutting-edge deep learning algorithm known as Residual Neural Network (ResNet) is utilised to train, validate, and test the acquired spectrograms. The confusion matrix, precision, recall, F1-score, and accuracy are exploited to evaluate the ResNet model's performance. ResNet's unique skip-connection technique minimises the underfitting/overfitting problem, providing an accuracy rate of up to 97.5%.
| Item Type: | Conference Proceedings |
|---|---|
| Additional Information: | This work was supported in parts by Engineering and Physical Sciences Research Council (EPSRC) grants: EP/T021020/1 and EP/T021063/1. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Shah, Mr Syed and Abbasi, Dr Qammer |
| Authors: | Badshah, S. S., Saeed, U., Momand, A., Shah, S. Y., Shah, S. I., Ahmad, J., Abbasi, Q. H., and Shah, S. A. |
| College/School: | College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering College of Science and Engineering > School of Engineering > Systems Power and Energy |
| ISBN: | 9781665409735 |
| Copyright Holders: | Copyright © 2022 IEEE |
| First Published: | First published in 2022 2nd International Conference of Smart Systems and Emerging Technologies (SMARTTECH) |
| Publisher Policy: | Reproduced in accordance with the publisher copyright policy |
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