Acceptance of a computer vision facilitated protocol to measure adherence to face mask use: a single-site, observational cohort study among hospital staff
Name
e062707.full.pdf
Description
Published version
Size
981.72 KB
Format
Adobe PDF
Checksum (MD5)
54016234681c5e19e2f654ef9223950a
Author(s)
Chai, Peter R
Rupp, Phillip
Huang, Hen-Wei
Chen, Jack
Vaz, Clint
Sinha, Anjali
Ehmke, Claas
Thomas, Akhil
Dadabhoy, Farah
Liang, Jia Y
Date Issued
December 2022
Journal
BMJ Open
Publisher
BMJ
Citation
Chai, Peter R, Rupp, Phillip, Huang, Hen-Wei, Chen, Jack, Vaz, Clint et al. 2022. "Acceptance of a computer vision facilitated protocol to measure adherence to face mask use: a single-site, observational cohort study among hospital staff." BMJ Open, 12 (12).
Version
Final published version
Abstract
ObjectivesMask adherence continues to be a critical public health measure to prevent transmission of aerosol pathogens, such as SARS-CoV-2. We aimed to develop and deploy a computer vision algorithm to provide real-time feedback of mask wearing among staff in a hospital.DesignSingle-site, observational cohort study.SettingAn urban, academic hospital in Boston, Massachusetts, USA.ParticipantsWe enrolled adult hospital staff entering the hospital at a key ingress point.InterventionsConsenting participants entering the hospital were invited to experience the computer vision mask detection system. Key aspects of the detection algorithm and feedback were described to participants, who then completed a quantitative assessment to understand their perceptions and acceptance of interacting with the system to detect their mask adherence.Outcome measuresPrimary outcomes were willingness to interact with the mask system, and the degree of comfort participants felt in interacting with a public facing computer vision mask algorithm.ResultsOne hundred and eleven participants with mean age 40 (SD15.5) were enrolled in the study. Males (47.7%) and females (52.3%) were equally represented, and the majority identified as white (N=54, 49%). Most participants (N=97, 87.3%) reported acceptance of the system and most participants (N=84, 75.7%) were accepting of deployment of the system to reinforce mask adherence in public places. One third of participants (N=36) felt that a public facing computer vision system would be an intrusion into personal privacy.Public-facing computer vision software to detect and provide feedback around mask adherence may be acceptable in the hospital setting. Similar systems may be considered for deployment in locations where mask adherence is important.
MIT Department
Koch Institute for Integrative Cancer Research at MIT
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution NonCommercial License 4.0
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DOI of Published Version
https://doi.org/10.1136/bmjopen-2022-062707
