Web-based Non-contact Edge Computing Solution for Suspected COVID-19 Infection Classification Model
DOI:
https://doi.org/10.13052/jwe1540-9589.2242Keywords:
Edge computing, COVID-19 classification, Non-Contact Bio-sensor, Artificial Intelligence, Machine LearningAbstract
The recent outbreak of the COVID-19 coronavirus pandemic has necessitated the development of web-based, non-contact edge analytics solutions. Non-contact sensors serve as the interface between web servers and edge analytics through web engineering technology. The need for an edge device classification model that can identify COVID-19 patients based on early symptoms has become evident. In particular a non-contact implementation of such a classification model is required to efficiently prevent viral infection and minimize cross-infection. In this work, we investigate the use of diverse non-contact biosensors (e.g., remote photoplethysmography, radar, and infrared sensors) for reducing effective physical contact with patients and for measuring their biometric data and vital signs. We further explain a classification method for suspected COVID-19 infection based on the measured vital signs and symptoms. The results of this study can be applied in patient classification by mobile-based edge computing applications. The correlation between symptoms comprising cough, sore throat, fever, headache, myalgia, and arthralgia are analyzed in the model. We implement a machine learning classification model using vital signs for performance evaluation, and propose an ensemble model realized by fine-tuning the high-performing classification models. The proposed ensemble model successfully distinguishes suspected patients with an accuracy, area under curve, and F1 scores of 94.4%, 98.4%, and 94.4%, respectively.
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