Web-based Non-contact Edge Computing Solution for Suspected COVID-19 Infection Classification Model





Edge computing, COVID-19 classification, Non-Contact Bio-sensor, Artificial Intelligence, Machine Learning


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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Author Biographies

Tae-Ho Hwang, Gachon University, SeongnamSi, South Korea

Tae-Ho Hwang received a B.Sc. degree in nuclear engineering, mechanical engineering and an MBA M.Sc. degree from Hanyang University, Seoul, South Korea in 1985 and 1991, respectively. He worked as a system engineer at Samsung Electronics from 1985 to 1991. Since 2020, he has been participating in the Ph.D. degree program at the Cognitive Computing Lab, Department of Computer Engineering, Gachon University. His research interests include big data, IoT, artificial intelligence, and deep learning.

KangYoon Lee, Gachon University, SeongnamSi, South Korea

KangYoon Lee received a B.Sc. degree in electronics engineering and an M.Sc. degree in computer science from Yonsei University, Seoul, South Korea in 1986 and 1996, respectively. He received a Ph.D. degree in IT policy management from Soongsil University, Seoul, South Korea, in 2010.

From 2008 to 2014, he was a Director at the IBM Korea Lab for Ubiquitous Computing and Software Solutions, and was promoted to leader of the IBM Watson Business Unit, Korea in 2014. Since 2016, he has been a professor with the Computer Engineering Department, Gachon University, IT College. His research interests include cognitive computing, healthcare advisor, IoT platforms, and industry transformation.

Dr. Lee is a director of the Korean Society for Internet Information (KSII), and, since 2019, he has been vice president of the Korea Bigdata Society.


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How to Cite

Hwang, T.-H. ., & Lee, K. . (2023). Web-based Non-contact Edge Computing Solution for Suspected COVID-19 Infection Classification Model. Journal of Web Engineering, 22(04), 597–614. https://doi.org/10.13052/jwe1540-9589.2242