Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review

Saeed, U., Shah, S. Y., Ahmad, J., Imran, M. A. , Abbasi, Q. H. and Shah, S. A. (2022) Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review. Journal of Pharmaceutical Analysis, 12(2), pp. 193-204. (doi: 10.1016/j.jpha.2021.12.006) (PMID:35003825) (PMCID:PMC8724017)

Abstract

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused the coronavirus disease 2019 (COVID-19) pandemic, has affected more than 400 million people worldwide. With the recent rise of new Delta and Omicron variants, the efficacy of the vaccines has become an important question. The goal of various studies has been to limit the spread of the virus by utilizing wireless sensing technologies to prevent human-to-human interactions, particularly for healthcare workers. In this paper, we discuss the current literature on invasive/contact and non-invasive/non-contact technologies (including Wi-Fi, radar, and software-defined radio) that have been effectively used to detect, diagnose, and monitor human activities and COVID-19 related symptoms, such as irregular respiration. In addition, we focused on cutting-edge machine learning algorithms (such as generative adversarial networks, random forest, multilayer perceptron, support vector machine, extremely randomized trees, and k-nearest neighbors) and their essential role in intelligent healthcare systems. Furthermore, this study highlights the limitations related to non-invasive techniques and prospective research directions.

• Item Type: Articles
• Keywords: Machine learning, COVID-19, Non-invasive healthcare, Non-contact sensing, Artificial intelligence
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Imran, Professor Muhammad and Abbasi, Professor Qammer
• Authors: Saeed, U., Shah, S. Y., Ahmad, J., Imran, M. A., Abbasi, Q. H., and Shah, S. A.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: Journal of Pharmaceutical Analysis
• Publisher: Elsevier
• ISSN: 2095-1779
• ISSN (Online): 2214-0883
• Published Online: 04 January 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in Journal of Pharmaceutical Analysis 12(2): 193-204
• Publisher Policy: Reproduced under a Creative Commons license