Physiological Monitoring Of Front-Line Caregivers For Cv-19 Symptoms: Multi-Resolution Analysis & Convolutional-Recurrent Networks

Abstract

Due to easy transmission of the COVID-19, a crucial step is the effective screening of the front-line caregivers are one of the most vulnerable populations for early signs and symptoms, resembling the onset of the disease. Our aim in this paper is to track a combination of biomarkers in our ubiquitous experimental setup to monitor the human participants’ operating system to predict the likelihood of the viral infection symptoms during the next 2 days using a mobile app, and an unobtrusive wearable ring to track their physiological indicators and self-reported symptoms. we propose a multi-resolution signal processing and modeling method to effectively characterize the changes in those physiological indicators. In this way, we decompose the 1-D input windowed time-series in multi-resolution (i.e. 2-D spectro-temporal) space. Then, we fitted our proposed deep learning architecture that combines recurrent neural network (RNN) and convolutional neural network (CNN) to incorporate and model the sequence of multi-resolution snapshots in 3-D time-series space. The CNN is used to objectify the underlying features in each of the 2D spectro-temporal snapshots, while the RNN is utilized to track the temporal dynamic behavior of the snapshot sequences to predict the patients’ COVID-19 related symptoms. As the experimental results show, our proposed architecture with the best configuration achieves 87.53% and 95.12% average accuracy in predicting the COVID-19 related symptoms.