Related Experiment Video
Updated: Oct 7, 2025
A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program
Published on: April 19, 2019
Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review
Umer Saeed1, Syed Yaseen Shah2, Jawad Ahmad3
1Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5FB, UK.
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.
Insights
Wireless sensing technologies and machine learning can detect COVID-19 symptoms like irregular breathing. These non-contact methods aid in limiting virus spread, especially for healthcare workers, by monitoring human activities.
Area of Science:
- Biomedical Engineering
- Computer Science
- Public Health
Background:
- The COVID-19 pandemic, caused by SARS-CoV-2, has impacted millions globally.
- Emerging variants like Delta and Omicron raise concerns about vaccine efficacy.
- Limiting human-to-human interaction is crucial for pandemic control, particularly in healthcare settings.
Purpose of the Study:
- To review wireless sensing technologies for detecting and monitoring COVID-19 symptoms.
- To explore the role of machine learning in intelligent healthcare systems for disease management.
- To identify limitations and future research directions for non-invasive monitoring techniques.
Main Methods:
- Literature review of invasive/contact and non-invasive/non-contact wireless sensing technologies (Wi-Fi, radar, SDR).
- Analysis of machine learning algorithms (GANs, Random Forest, MLP, SVM, ET, k-NN) applied to healthcare.
- Focus on detecting human activities and COVID-19 symptoms like irregular respiration.
Main Results:
- Wireless sensing technologies effectively detect, diagnose, and monitor human activities and symptoms.
- Machine learning algorithms are integral to intelligent healthcare systems for analyzing sensor data.
- Non-invasive techniques show promise but have associated limitations.
Conclusions:
- Wireless sensing and AI offer innovative solutions for remote patient monitoring and infection control.
- Further research is needed to overcome limitations of non-invasive techniques.
- These technologies can support healthcare workers and mitigate virus transmission.
