UWB Radar Sensing for Respiratory Monitoring Exploiting Time- Frequency Spectrograms

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 req...

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Published in2022 2nd International Conference of Smart Systems and Emerging Technologies (SMARTTECH) pp. 136 - 141
Main Authors Badshah, Syed Salman, Saeed, Umer, Momand, Asadullah, Shah, Syed Yaseen, Shah, Syed Ikram, Ahmad, Jawad, Abbasi, Qammer H., Shah, Syed Aziz
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2022
Subjects
COVID-19
deep learning
ResNet
Sensors
Spectrogram
Time-frequency analysis
Ultra wideband radar
UWB radar sensor
Wireless communication
wireless healthcare
Wireless sensor networks
XeThru X4M200
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Summary: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%.
DOI:10.1109/SMARTTECH54121.2022.00040