Wirelessly sensor technology for verifiable decontamination of N95 respirator masks
In response to the N95 mask shortage caused by the COVID-19 pandemic, the 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 properdecontamination conditions. This NSF RAPID SaTC founded research project (Award #2031077) tackles the urgent research needs to assure and inform healthcare workers of the conditions necessary for mask decontamination processes in ovens with known risks of non-uniform heating, and rapidly encourage the deployment of highly scalable and trustworthy sensor network technology for monitoring and verification of individual mask decontamination. The research has particular focus to small clinics, rural facilities, and developing countries that lack convenient access to dedicated N95 mask decontamination systems.
Publications
- VeriMask: Facilitating Decontamination of N95 Masks in the
COVID-19 Pandemic: Challenges, Lessons Learned, and
Safeguarding the Future
Authors: Yan Long, Alexander Curtiss, Sara Rampazzi, Josiah Hester, Kevin Fu
Journal: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (ACM IMWUT)
Download Paper Open-soure Code
- Automating Decontamination of N95Masks for Frontline Workers in COVID-19 Pandemic
Authors: Yan Long, Alexander Curtiss, Sara Rampazzi, Josiah Hester, Kevin Fu
Conference: COVID-19 Pandemic Response Poster Session, SenSys 2020 (BEST POSTER RUNNER-UP AWARD)
Download Paper
Last News
- 07/16/2021
- Our project full paper has been accepted to ACM IMWUT !
- 02/16/2021
- Prof. Josiah Hester release an interview on the project updates
- 01/24/2021
- Prof. Kevin Fu has been interviewed by Tech Time - Click on Detroit news. Here the interview video
- 11/20/2020
- We won the best poster runner-up award for COVID-19 Response Research at Sensys 2020!
- 11/17/2020
- Ph.D. student Yan Long presents the research at Sensys2020
- 10/16/2020
- Prof. Sara Rampazzi presents the project at the COVID-19 Research Lightning Round Webinar organized by COVID Information Commons
- 10/14/2020
- Our project poster has been accepted to SenSys 2020 COVID-19 Pandemic Response track!
Team
PI: Prof. Kevin Fu (University of Michigan)
Co-PI: Prof. Sara Rampazzi (University of Florida)
Prof. Josiah Hester (Northwestern University)
Yan Long, Ph.D. student (University of Michigan)
Alex Curtiss, Ph.D. student (Northwestern University)