UWB Radar Sensing for Respiratory Monitoring Exploiting Time-Frequency Spectrograms

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%.

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Identifiers

DOI

https://dx.doi.org/10.1109/SMARTTECH54121.2022.00040

Repository URI

http://eprints.gla.ac.uk/263417/