Infra-Red Imaging to Detect Respirator Leak in Healthcare Workers During Fit-Testing Clinic
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 gradie...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10309158/ |
| _version_ | 1827308190938693632 |
|---|---|
| author | Darius Chapman Campbell Strong Kathryn D Tiver Dhani Dharmaprani Even Jenkins Anand N Ganesan |
| author_facet | Darius Chapman Campbell Strong Kathryn D Tiver Dhani Dharmaprani Even Jenkins Anand N Ganesan |
| author_sort | Darius Chapman |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T18:54:11Z |
| format | Article |
| id | doaj.art-a280da26bdc040298cd2fd5da1abac87 |
| institution | Directory Open Access Journal |
| issn | 2644-1276 |
| language | English |
| last_indexed | 2024-04-24T18:54:11Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Engineering in Medicine and Biology |
| spelling | doaj.art-a280da26bdc040298cd2fd5da1abac872024-03-26T17:46:30ZengIEEEIEEE Open Journal of Engineering in Medicine and Biology2644-12762024-01-01519820410.1109/OJEMB.2023.333029210309158Infra-Red Imaging to Detect Respirator Leak in Healthcare Workers During Fit-Testing ClinicDarius Chapman0https://orcid.org/0000-0002-3101-0178Campbell Strong1Kathryn D Tiver2https://orcid.org/0000-0003-3340-2113Dhani Dharmaprani3https://orcid.org/0000-0003-4660-0119Even Jenkins4Anand N Ganesan5https://orcid.org/0000-0002-2734-3025College of Medicine and Public Health, Flinders University, Adelaide, SA, AustraliaCollege of Medicine and Public Health, Flinders University, Adelaide, SA, AustraliaCollege of Medicine and Public Health, Flinders University, Adelaide, SA, AustraliaCollege of Medicine and Public Health, Flinders University, Adelaide, SA, AustraliaCollege of Medicine and Public Health, Flinders University, Adelaide, SA, AustraliaCollege of Medicine and Public Health, Flinders University, Adelaide, SA, AustraliaObjective: 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.https://ieeexplore.ieee.org/document/10309158/Infra-red imagingmachine learningoccupational hygieneP2 respiratorsrespirator leak detection |
| spellingShingle | Darius Chapman Campbell Strong Kathryn D Tiver Dhani Dharmaprani Even Jenkins Anand N Ganesan Infra-Red Imaging to Detect Respirator Leak in Healthcare Workers During Fit-Testing Clinic IEEE Open Journal of Engineering in Medicine and Biology Infra-red imaging machine learning occupational hygiene P2 respirators respirator leak detection |
| title | Infra-Red Imaging to Detect Respirator Leak in Healthcare Workers During Fit-Testing Clinic |
| title_full | Infra-Red Imaging to Detect Respirator Leak in Healthcare Workers During Fit-Testing Clinic |
| title_fullStr | Infra-Red Imaging to Detect Respirator Leak in Healthcare Workers During Fit-Testing Clinic |
| title_full_unstemmed | Infra-Red Imaging to Detect Respirator Leak in Healthcare Workers During Fit-Testing Clinic |
| title_short | Infra-Red Imaging to Detect Respirator Leak in Healthcare Workers During Fit-Testing Clinic |
| title_sort | infra red imaging to detect respirator leak in healthcare workers during fit testing clinic |
| topic | Infra-red imaging machine learning occupational hygiene P2 respirators respirator leak detection |
| url | https://ieeexplore.ieee.org/document/10309158/ |
| work_keys_str_mv | AT dariuschapman infraredimagingtodetectrespiratorleakinhealthcareworkersduringfittestingclinic AT campbellstrong infraredimagingtodetectrespiratorleakinhealthcareworkersduringfittestingclinic AT kathryndtiver infraredimagingtodetectrespiratorleakinhealthcareworkersduringfittestingclinic AT dhanidharmaprani infraredimagingtodetectrespiratorleakinhealthcareworkersduringfittestingclinic AT evenjenkins infraredimagingtodetectrespiratorleakinhealthcareworkersduringfittestingclinic AT anandnganesan infraredimagingtodetectrespiratorleakinhealthcareworkersduringfittestingclinic |