Predicting Fit of Filtering Facepiece Respirators Through New Face Anthropometry and 3D Face Shape Acquisition

Abstract

This research investigated the relationship between face shape and respirator fit, with a focus on enhancing the fit and design of filtering facepiece respirators (FFRs). The study addressed the need for improved respirator fit, particularly in occupational settings where respiratory protection is paramount for safeguarding workers' health. Combining anthropometric analysis, three-dimensional (3D) scanning technology, quantitative fit testing, and predictive modeling, this research assessed the impact of face shape on respirator fit. It examined the limitations of traditional two-dimensional anthropometric measures in predicting FFR fit and proposed a novel framework based on 3D-derived face shapes and dimensions. Key findings highlighted the importance of predicting respirator fit based on diverse facial shapes and sizes. By integrating face anthropometric and geometric data into respirator design processes, manufacturers can develop more ergonomic and effective respiratory protective equipment. Such predictive capabilities can aid individuals in selecting respirators that are more likely to provide a secure fit, thereby enhancing the overall effectiveness of protection and reducing the risk of exposure to airborne hazards. The implications of this research extend beyond occupational safety and health, encompassing broader public health considerations, particularly in the context of infectious disease outbreaks like the COVID-19 pandemic. By advancing the understanding of respirator fit, this research contributes to the development of evidence-based practices for respiratory protection, ultimately enhancing the well-being and safety of workers worldwide.