ABSTRACT. Despite the relevance of Internet of Things-based health monitoring systems, artificial intelligence-driven diagnostic algorithms, and body area sensor networks in COVID-19 prevention, screening, and treatment, only limited research has been conducted on this topic. In this article, I cumulate previous research findings indicating that wearable Internet of Medical Things technologies can accurately assess COVID-19 symptoms. I contribute to the literature on smart sensor-based Internet of Medical Things and healthcare-integrated technologies by showing that machine learning algorithms and deep learning techniques can assist in COVID-19 prognosis. Throughout January 2022, I performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “COVID-19” + “Internet of Things-based health monitoring systems,” “artificial intelligence-driven diagnostic algorithms,” and “body area sensor networks.” As I inspected research published between 2020 and 2022, only 152 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, I decided upon 28, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, Distiller SR, and MMAT.

Keywords: Internet of Medical Things; COVID-19; body area sensor network

How to cite: Bowles, P. (2022). “Internet of Things-based Health Monitoring Systems, Artificial Intelligence-driven Diagnostic Algorithms, and Body Area Sensor Networks in COVID-19 Prevention, Screening, and Treatment,” American Journal of Medical Research 9(1): 113–128. doi: 10.22381/ajmr9120228.

Received 26 January 2022 • Received in revised form 19 April 2022
Accepted 24 April 2022 • Available online 30 April 2022

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