Innovation in Respirator Design, Research, & Protection: A model of predictive fit for occupational safety and health

• Published in: Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2023-09, Vol.67 (1), p.821-821
• Main Authors: Griffin, Linsey, Yu, Minji, Sokolowski, Susan, Arnold, Susan, Durfee, William K.
• Format: Article
• Language: English
• ISSN: 1071-1813; 2169-5067
• DOI: 10.1177/21695067231192265

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.