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

Umer Saeed; Syed Yaseen Shah; Jawad Ahmad; Muhammad Ali Imran; Qammer H. Abbasi; Syed Aziz Shah

doi:10.1016/j.jpha.2021.12.006

Volume 12 Issue 2

May 2022

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Article Navigation > Journal of Pharmaceutical Analysis > 2022 > 12(2): 193-204

Umer Saeed, Syed Yaseen Shah, Jawad Ahmad, Muhammad Ali Imran, Qammer H. Abbasi, Syed Aziz Shah. Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review[J]. Journal of Pharmaceutical Analysis, 2022, 12(2): 193-204. doi: 10.1016/j.jpha.2021.12.006

Citation:

Umer Saeed, Syed Yaseen Shah, Jawad Ahmad, Muhammad Ali Imran, Qammer H. Abbasi, Syed Aziz Shah. Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review[J]. Journal of Pharmaceutical Analysis, 2022, 12(2): 193-204. doi: 10.1016/j.jpha.2021.12.006

Citation:

Umer Saeed, Syed Yaseen Shah, Jawad Ahmad, Muhammad Ali Imran, Qammer H. Abbasi, Syed Aziz Shah. Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review[J]. Journal of Pharmaceutical Analysis, 2022, 12(2): 193-204. doi: 10.1016/j.jpha.2021.12.006

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

doi: 10.1016/j.jpha.2021.12.006

a. Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5FB, UK;
b. School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, G4 0BA, UK;
c. School of Computing, Edinburgh Napier University, Edinburgh, EH11 4BN, UK;
d. James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK

Received Date: Apr. 05, 2021
Accepted Date: Dec. 30, 2021
Rev Recd Date: Dec. 29, 2021
Publish Date: Jan. 04, 2022

Abstract

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.
- Artificial intelligence,
- Non-invasive healthcare,
- Machine learning,
- Non-contact sensing,
- COVID-19

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References(170)

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