SafeRespirator: Comprehensive database for N95 filtering facepiece respirator leak detection including infrared, RGB videos, and quantitative fit testing

Type de document

Études primaires

Année de publication

2024

Langue

Anglais

Titre de la revue

IEEE Access

Résumé

The COVID-19 pandemic underscored the challenges of performing mandatory Quantitative Fit Tests (QNFT) for healthcare professionals and the limitations of self-administered fit checks. To address this, it is crucial to develop faster and more efficient methods for detecting, locating, and quantifying leaks in Filtering Facepiece Respirators (FFRs), providing wearers with immediate feedback on their safety. Infrared (IR) technology, which relies on temperature variation analysis around the face seal, has proven effective for locating leaks but has not yet achieved automated quantification. This paper introduces a validated protocol for creating a comprehensive database to advance automatic leak detection. The database includes synchronized and calibrated IR and RGB video data, along with QNFT results, collected from 62 participants wearing four different N95 FFR models in four distinct positions. High-performance IR and RGB cameras were used to precisely capture temperature variations, while a PortaCount® instrument served as the reference for fit quantification. Preliminary results demonstrate the ability of IR imaging to accurately monitor temperature variations across the facial seal, paving the way for automated detection. This open-access database is available to the scientific community to drive innovation in respiratory protection research and beyond.

Mots-clés

Base de données, Data base, Essai d’ajustement, Fit test, Rayonnement infrarouge, Infrared radiation, Équipement de protection individuelle, Personal protective equipment, Détection des fuites, Leak detection, Masque N95, N95 mask, Santé et sécurité du travail, Occupational health and safety

Numéro de projet IRSST

n/a

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