American Journal of Medical Research 9(1), 2022
pp. 81–96, ISSN 2334-4814, eISSN 2376-4481
Internet of Medical Things-driven Remote Monitoring
Systems, Big Healthcare Data Analytics, and Wireless Body
Area Networks in COVID-19 Detection and Diagnosis
John Hudson*
ABSTRACT. The aim of this systematic review is to synthesize and analyze Internet
of Medical Things-driven remote monitoring systems, big healthcare data analytics,
and wireless body area networks in COVID-19 detection and diagnosis. With in-
creasing evidence of COVID-19 diagnostic applications, there is an essential demand
for comprehending whether remote rapid monitoring and diagnosis of suspected
COVID-19 individuals can be enabled by interconnected Internet of Medical Things
infrastructure and healthcare equipment. In this research, prior findings were
cumulated indicating that Internet of Medical Things can be leveraged in contact
tracing during the COVID-19 pandemic. I carried out a quantitative literature review
of ProQuest, Scopus, and the Web of Science throughout January 2022, with search
terms including “COVID-19” + “Internet of Medical Things-driven remote moni-
toring systems,” “big healthcare data analytics,” and “wireless body area networks.”
As I analyzed research published between 2020 and 2022, only 148 papers met
the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant
data), results unsupported by replication, undetailed content, or papers having quite
similar titles, I decided on 29, chiefly empirical, sources. Data visualization tools:
Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting
quality assessment tool: PRISMA. Methodological quality assessment tools include:
AXIS, Distiller SR, ROBIS, and SRDR.
Keywords: COVID-19; Internet of Medical Things; wireless body area network
How to cite: Hudson, J. (2022). “Internet of Medical Things-driven Remote Monitoring
Systems, Big Healthcare Data Analytics, and Wireless Body Area Networks in COVID-19
Detection and Diagnosis,” American Journal of Medical Research 9(1): 81–96. doi:
10.22381/ajmr9120226.
Received 26 January 2022 • Received in revised form 23 April 2022
Accepted 28 April 2022 • Available online 30 April 2022
*Deep Learning-based Sensing Technologies Laboratory at ISBDA, Leicester, England,
john.hudson@aa-er.org.
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