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%.
|Title of host publication||2022 2nd International Conference of Smart Systems and Emerging Technologies (SMARTTECH)|
|Number of pages||6|
|Publication status||Published - May 2022|
|Event||2022 2nd International Conference of Smart Systems and Emerging Technologies (SMARTTECH) - Riyadh, Saudi Arabia|
Duration: 9 May 2022 → 11 May 2022
|Name||2022 2nd International Conference of Smart Systems and Emerging Technologies (SMARTTECH)|
|Conference||2022 2nd International Conference of Smart Systems and Emerging Technologies (SMARTTECH)|
|Period||9/05/22 → 11/05/22|
Bibliographical noteFunding Information:
This work was supported in parts by Engineering and Physical Sciences Research Council (EPSRC) grants: EP/T021020/1 and EP/T021063/1.
© 2022 IEEE.
- Wireless communication
- Wireless sensor networks
- Time-frequency analysis
- Ultra wideband radar ,
- XeThru X4M200
- UWB radar sensor
- wireless healthcare
- deep learning