Enabling Real-Time Dashboards for Anxiety Risk Classification Using the Internet of Things

Shama Siddiqui, Farid Nait-Abdesselam, Anwar Ahmed Khan, Indrakshi Dey

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)

Abstract

The ubiquity of sensor technology and the Internet of Things prompted us to propose to develop a real-time digital dashboard to visualize the anxiety risks of populations during a pandemic, as in the case of COVID-19. To this end, here we provide an end-to-end communication architecture to detect physiological data related to heart rate, blood pressure, and SPO2, using wearable sensors and communicate them to remote 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, i.e., 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.

Original languageEnglish
JournalProceedings - IEEE Global Communications Conference, GLOBECOM
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event2021 IEEE Global Communications Conference, GLOBECOM 2021 - Madrid, Spain
Duration: 07 Dec 202111 Dec 2021

Keywords

  • environmental sensors
  • Internet of things
  • real-time dashboards
  • smart health
  • wearable sensors

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