UWB Radar Sensing for Respiratory Monitoring Exploiting Time-Frequency Spectrograms

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, Professor 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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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
307829Quantum-Inspired Imaging for Remote Monitoring of Health & Disease in Community HealthcareJonathan CooperEngineering and Physical Sciences Research Council (EPSRC)EP/T021020/1ENG - Biomedical Engineering