Title: Predicting respirator size and fit from 2D images

Authors: Eric Biagiotti; Medhat Korna; Daniel O. Rice; Daniel Barker

Addresses: Technology Solutions Experts, Inc., 209 West Central Street, Suite 202, Natick, MA 01760, USA ' Technology Solutions Experts, Inc., 209 West Central Street, Suite 202, Natick, MA 01760, USA ' Technology Solutions Experts, Inc., 209 West Central Street, Suite 202, Natick, MA 01760, USA ' Edgewood Chemical Biological Center, Research and Technology Directorate, ATTN: RDCB-DRP-R, Bldg. 3400, Aberdeen Proving Ground, MD 21010-5424, USA

Abstract: Individuals rely heavily on the efficacy of respirators when there is potential for chemical, biological, radiological, or nuclear threats (CBRN). The US Department of Defense (DoD) requires personnel to undergo time-consuming fit tests for full face respirators, which are a critical component of the personal protective equipment (PPE) ensemble. The quality of respirator fit directly contributes to its effectiveness. Leveraging the ubiquity and capabilities of mobile devices, machine learning, and advances in computerised 3D modelling, we seek to make this process simpler, faster, and more accurate. This paper introduces the mask analysis and size quantification (MASQ) framework: an extensible mobile-based semi-automated system that: 1) combines 2D images of a subject's head, an existing 3D headform model generation approach, and an analytic model to recommend a size; 2) establishes a platform for future research of the shape and anthropometric features of the human face and respirator sizing and fit effectiveness.

Keywords: 3DMM; 3D morphable model; landmark detection; camera calibration; mobile devices; anthropometry; chemical, biological, radiological, or nuclear threats; CBRN; respirator; sizing; fit; machine learning; 2D images; mask analysis and size quantification; MASQ.

DOI: 10.1504/IJHFMS.2019.105420

International Journal of Human Factors Modelling and Simulation, 2019 Vol.7 No.2, pp.137 - 151

Received: 29 Jan 2019
Accepted: 04 Jun 2019
Published online: 28 Feb 2020 *

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