Automating decontamination of N95 masks for frontline workers in COVID-19 pandemic: Poster abstract

Yan Long, Alexander Curtiss, Sara Rampazzi, Josiah Hester, Kevin Fu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In response to the N95 mask shortage caused by the COVID-19 pandemic, the US CDC has recognized moist-heat as one of the most effective and accessible methods for decontaminating N95 masks for reuse. However, it is challenging to reliably deploy this technique in healthcare settings due to a lack of specialized equipment capable of ensuring proper decontamination conditions. To this end, we developed a wireless sensor platform for moist-heat decontamination process verification, capable of monitoring hundreds of masks simultaneously in commercially available heating systems. Our easy-to-use, low-power, low-cost, scalable platform can be broadly deployed to protect front-line healthcare workers by lowering their risk of infection from reused N95 masks.

Original languageEnglish (US)
Title of host publicationSenSys 2020 - Proceedings of the 2020 18th ACM Conference on Embedded Networked Sensor Systems
PublisherAssociation for Computing Machinery, Inc
Pages747-749
Number of pages3
ISBN (Electronic)9781450375900
DOIs
StatePublished - Nov 16 2020
Event18th ACM Conference on Embedded Networked Sensor Systems, SenSys 2020 - Virtual, Online, Japan
Duration: Nov 16 2020Nov 19 2020

Publication series

NameSenSys 2020 - Proceedings of the 2020 18th ACM Conference on Embedded Networked Sensor Systems

Conference

Conference18th ACM Conference on Embedded Networked Sensor Systems, SenSys 2020
CountryJapan
CityVirtual, Online
Period11/16/2011/19/20

Keywords

  • COVID-19
  • N95 masks decontamination
  • wireless sensor

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Computer Networks and Communications

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