ABSTRACT. In this article, we cumulate previous research findings indicating that machine learning algorithms are pivotal in COVID-19 detection and monitoring. We contribute to the literature on COVID-19 diagnosis, monitoring, and treatment remotely by showing that wearable systems, biosensors, and Internet of Medical Things devices can track COVID-19 symptoms. Throughout February 2022, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “COVID-19” + “machine and deep learning techniques,” “body sensor networks,” and “Internet of Things-based smart healthcare systems.” As we inspected research published between 2020 and 2022, only 146 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 29, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, MMAT, and ROBIS.

Keywords: remote patient monitoring; body sensor network; COVID-19

How to cite: Stone, D., Michalkova, L., and Machova, V. (2022). “Machine and Deep Learning Techniques, Body Sensor Networks, and Internet of Things-based Smart Healthcare Systems in COVID-19 Remote Patient Monitoring,” American Journal of Medical Research 9(1): 97–112. doi: 10.22381/ajmr9120227.

Received 27 February 2022 • Received in revised form 25 April 2022
Accepted 28 April 2022 • Available online 30 April 2022