Infra-red imaging to detect respirator leak in healthcare workers during fit-testing clinic.

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Objective: This study addressed the problem of objectively detecting leaks in P2 respirators at point of use, an essential component for healthcare workers' protection. To achieve this, we explored the use of infra-red (IR) imaging combined with machine learning algorithms on the thermal gradient across the respirator during inhalation. Results: The study achieved high accuracy in predicting pass or fail outcomes of quantitative fit tests for flat-fold P2 FFRs. The IR imaging methods surpassed the limitations of self fit-checking. Conclusions: The integration of machine learning and IR imaging on the respirator itself demonstrates promise as a more reliable alternative for ensuring the proper fit of P2 respirators. This innovative approach opens new avenues for technology application in occupational hygiene and emphasizes the need for further validation across diverse respirator styles. Significance Statement: Our novel approach leveraging infra-red imaging and machine learning to detect P2 respirator leaks represents a critical advancement in occupational safety and healthcare workers' protection.
Original languageEnglish
Pages (from-to)198-204
Number of pages7
JournalIEEE Open Journal of Engineering in Medicine and Biology
Early online date6 Nov 2023
Publication statusPublished - 2024


  • Hidden Markov models
  • Imaging
  • Infra-red imaging
  • Machine learning
  • Matlab
  • Medical services
  • Occupational hygiene
  • P2 respirators
  • Respirator leak detection
  • Testing
  • Training
  • machine learning
  • occupational hygiene
  • respirator leak detection


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