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<p>The ubiquity of sensor technology and the Internet
of Things prompted us to propose to develop an end-to-end
communication architecture for real-time digital dashboards to
visualize the anxiety risks of a population during a pandemic, as
in the case of COVID-19. Such an architecture can be regarded
as the next-generation anxiety risk classification mean for the
healthcare industry 4.0 as it will be capable of generating
automated and quick actions through the use of analytics on the
collected data and predefined thresholds. Based on Internet of
Things and wearable healthcare sensors, the proposed end-to-end
communication architecture is capable of detecting physiological
data related to heart rate, blood pressure, and SPO2, and
communicate them to remote cloud servers. Based on this
collected data, the centralized dashboard will classify in real
time the patients of each geographic region involved according
to a specific attribute, namely: normal, mild, moderate, high,
severe, or extreme. In addition, we also propose to incorporate
the emerging technologies of Space Time Frequency Spreading
(STFS) and Space-Time Spreading-Aided Indexed Modulation
(STS-IM) for the design of the communication links. It has
been found that the integration of STFS and STS-IM promises
to reduce the likelihood of data disruption for the proposed
architecture.</p>
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