2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDCOVID-IP-2.7
Paper Title PHYSIOLOGICAL MONITORING OF FRONT-LINE CAREGIVERS FOR CV-19 SYMPTOMS: MULTI-RESOLUTION ANALYSIS & CONVOLUTIONAL-RECURRENT NETWORKS
Authors Omid Dehzangi, Paria Jeihouni, Victor Finomore, Ali Rezai, West Virginia University, United States
SessionCOVID-IP-2: COVID Related Image Processing 2
LocationArea A
Session Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Poster
Topic COVID-Related Image Processing: COVID-related image processing
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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.