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

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.

Keywords: Artificial intelligence; COVID-19; Machine learning; Non-contact sensing; Non-invasive healthcare.

Graphical abstract

Fig. 1

Coronavirus 2019 (COVID-19) symptom monitoring system through wireless sensing. Distinct sensors are connected to the body and information is transmitted through a gateway such as a cell phone. Actions are applied by caretakers according to the conveyed information through sensors.

Fig. 2

COVID-19 symptom detection and monitoring system through distinct invasive/non-invasive technologies merged with intelligent AI techniques. X-ray: X-radiation; CT: computerized tomography; RF: radio-frequency.

Fig. 3

Camera-based breathing rate monitoring approach (reprinted from Ref. [82] with permission). ROI: region of interest.

Fig. 4

Experimental setup to monitor abnormal respiratory using ultrasound signals (reprinted from Ref. [99] with permission).

Fig. 5

Sample images of normal person and patients with COVID-19 (left) and histograms of the images (right). CT scanning of (A) patients with COVID-19 and (B) normal person. X-ray of (C) patients with COVID-19 and (D) normal person. (Reprint from Ref. [100] with permission).

Fig. 6

Abnormal respiratory monitoring system using Wi-Fi sensing technology (reprinted from Ref. [42] with permission).

Fig. 7

Generic framework toward COVID-19 symptom detection.

Fig. 8

Distinct effective machine learning classifiers used to detect and monitor symptoms of COVID-19. G: generator; D: discriminatror.