TechRxiv
IoTJ-SI-Paper.pdf (2.01 MB)

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

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preprint
posted on 10.05.2021, 07:12 by Shama Siddiqui, Farid Nait-Abdesselam, Anwar Ahmed Khan, Shamsul Arfin Qasmi, Indrakshi Dey

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.

History

Email Address of Submitting Author

naf@umkc.edu

ORCID of Submitting Author

0000-0002-5042-5387

Submitting Author's Institution

University of Missouri Kansas City

Submitting Author's Country

United States of America