A Covid-19 viral transmission prevention system for embedded devices utilising deep learning

Mihai Penica, Reenu Mohandas, Mangolika Bhattacharya, Karl Vancamp, Martin Hayes, Eoin O'Connell

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The coronavirus pandemic (COVID-19) has created an urgent need for different monitoring systems to prevent viral transmission because of its severity and contagious aspect. This paper proposes design and implementation of a hardware-software solution that uses supervised machine learning algorithms to examine an individual and determine if he/she poses a viral transmission danger. The solution proposed was developed utilising an ARM embedded device along with different sensors to detect and monitor COVID-19 symptoms and, at the same time, to enforce wearing of a mask by using deep learning computer vision.

Original languageEnglish
Title of host publication2021 32nd Irish Signals and Systems Conference, ISSC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665434294
DOIs
Publication statusPublished - 10 Jun 2021
Event32nd Irish Signals and Systems Conference, ISSC 2021 - Athlone, Ireland
Duration: 10 Jun 202111 Jun 2021

Publication series

Name2021 32nd Irish Signals and Systems Conference, ISSC 2021

Conference

Conference32nd Irish Signals and Systems Conference, ISSC 2021
Country/TerritoryIreland
CityAthlone
Period10/06/2111/06/21

Keywords

  • artificial intelligence
  • COVID-19 symptoms
  • covid19 viral transmission
  • health monitoring
  • raspberry pi

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