Real-Time Internet of Things Enabled Dashboard for Next Generation
Anxiety Risk Classification
Abstract
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.