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

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

Research output: Contribution to journalReview articlepeer-review

9 Citations (Scopus)
42 Downloads (Pure)


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 review article, 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.

Original languageEnglish
Pages (from-to)193-204
Number of pages12
JournalJournal of Pharmaceutical Analysis
Issue number2
Early online date4 Jan 2022
Publication statusPublished - Apr 2022

Bibliographical note

© 2022 The Authors. Published by Elsevier B.V. on behalf of Xi’an Jiaotong University. This is an open access article under the CC BY license (


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


Dive into the research topics of 'Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review'. Together they form a unique fingerprint.

Cite this