Abstract
Improving the fit of a half-mask respirator can be achieved by developing a design, fit, and sizing strategy to fit the faces of the general population or a specific group such as race, age group, or occupation. The purpose of this study was to define respirator fit based on the body product relationship and to develop a new set of facial landmarks and measurements for half-mask respirator design. 3D scan data and quantitative fit factor scores from 47 healthcare workers and 9 researchers in healthcare-related fields were utilized to investigate the relationship of new anthropometry measurements to respirator fit. A mask fit association model was validated through logistic regression. The respirator fit prediction model incorporating highly correlated face measurements opens the possibility of developing a system for judging respirator fit success and failure based on face dimensions; it can be integrated with automated measuring technologies and machine learning.
Original language | English (US) |
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Pages (from-to) | 821 |
Number of pages | 1 |
Journal | Proceedings of the Human Factors and Ergonomics Society |
Volume | 67 |
Issue number | 1 |
DOIs | |
State | Published - 2023 |
Event | 67th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2023 - Columbia, United States Duration: Oct 23 2023 → Oct 27 2023 |
Bibliographical note
Publisher Copyright:© 2023 Human Factors and Ergonomics Society.
Keywords
- anthropometry
- Personal Protective Equipment
- PPE
- respirator fit