Abstract
Contactless or non-invasive technology for the monitoring of anomalies in an inconspicuous and distant environment has immense significance in health-related applications, in particular COVID-19 symptoms detection, diagnosis, and monitoring. Contactless methods are crucial specifically during the COVID-19 epidemic as they require the least amount of involvement from infected individuals as well as healthcare personnel. According to recent medical research studies regarding coronavirus, individuals infected with novel COVID-19-Delta variant undergo elevated respiratory rates due to extensive infection in the lungs. This appalling situation demands constant real-time monitoring of respiratory patterns, which can help in avoiding any pernicious circumstances. In this paper, an Ultra-Wideband RADAR sensor enquote XeThru X4M200 is exploited to capture vital respiratory patterns. In the low and high frequency band, X4M200 operates within the 6.0-8.5 GHz and 7.25-10.20 GHz band, respectively. The experimentation is conducted on six distinct individuals to replicate a realistic scenario of irregular respiratory rates. The data is obtained in the form of spectrograms by carrying out normal (eupnea) and abnormal (tachypnea) respiratory. The collected spectrogram data is trained, validated, and tested using a cutting-edge deep learning technique called Residual Neural Network or ResNet. The trained ResNet model's performance is assessed using the confusion matrix, precision, recall, F1-score, and classification accuracy. The unordinary skip connection process of the deep ResNet algorithm significantly reduces the underfitting and overfitting problem, resulting in a classification accuracy rate of up to 90%.