An image dataset for surveillance of personal protective equipment adherence in healthcare
Abstract Proper personal protective equipment (PPE) use is critical to prevent disease transmission to healthcare providers, especially those treating patients with a high infection risk. To address the challenge of monitoring PPE usage in healthcare, computer vision has been evaluated for tracking...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-04355-0 |
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| author | Wanzhao Yang Mary S. Kim Genevieve J. Sippel Aaron H. Mun Kathleen H. McCarthy Beomseok Park Aleksandra Sarcevic Marius George Linguraru Ivan Marsic Randall S. Burd |
| author_facet | Wanzhao Yang Mary S. Kim Genevieve J. Sippel Aaron H. Mun Kathleen H. McCarthy Beomseok Park Aleksandra Sarcevic Marius George Linguraru Ivan Marsic Randall S. Burd |
| author_sort | Wanzhao Yang |
| collection | DOAJ |
| description | Abstract Proper personal protective equipment (PPE) use is critical to prevent disease transmission to healthcare providers, especially those treating patients with a high infection risk. To address the challenge of monitoring PPE usage in healthcare, computer vision has been evaluated for tracking adherence. Existing datasets for this purpose, however, lack a diversity of PPE and nonadherence classes, represent single not multiple providers, and do not depict dynamic provider movement during patient care. We introduce the Resuscitation Room Personal Protective Equipment (R2PPE) dataset that bridges this gap by providing a realistic portrayal of diverse PPE use by multiple interacting individuals in a healthcare setting. This dataset contains 26 videos, 10,034 images and 123,751 bounding box annotations for 17 classes of PPE adherence and nonadherence for eyewear, masks, gowns, and gloves, and one additional head class. Evaluations using newly proposed metrics confirm R2PPE exhibits higher annotation density than three established general-purpose and medical PPE datasets. The R2PPE dataset provides a resource for developing computer vision algorithms for monitoring PPE use in healthcare. |
| format | Article |
| id | doaj-art-41538e06818f434f9a758286b8690e6d |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-41538e06818f434f9a758286b8690e6d2025-01-19T12:09:53ZengNature PortfolioScientific Data2052-44632025-01-0112111010.1038/s41597-024-04355-0An image dataset for surveillance of personal protective equipment adherence in healthcareWanzhao Yang0Mary S. Kim1Genevieve J. Sippel2Aaron H. Mun3Kathleen H. McCarthy4Beomseok Park5Aleksandra Sarcevic6Marius George Linguraru7Ivan Marsic8Randall S. Burd9Department of Electrical and Computer Engineering, Rutgers UniversityDivision of Trauma and Burn Surgery, Children’s National HospitalDivision of Trauma and Burn Surgery, Children’s National HospitalDivision of Trauma and Burn Surgery, Children’s National HospitalDivision of Trauma and Burn Surgery, Children’s National HospitalDepartment of Electrical and Computer Engineering, Rutgers UniversityCollege of Computing and Informatics, Drexel UniversitySheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National HospitalDepartment of Electrical and Computer Engineering, Rutgers UniversityDivision of Trauma and Burn Surgery, Children’s National HospitalAbstract Proper personal protective equipment (PPE) use is critical to prevent disease transmission to healthcare providers, especially those treating patients with a high infection risk. To address the challenge of monitoring PPE usage in healthcare, computer vision has been evaluated for tracking adherence. Existing datasets for this purpose, however, lack a diversity of PPE and nonadherence classes, represent single not multiple providers, and do not depict dynamic provider movement during patient care. We introduce the Resuscitation Room Personal Protective Equipment (R2PPE) dataset that bridges this gap by providing a realistic portrayal of diverse PPE use by multiple interacting individuals in a healthcare setting. This dataset contains 26 videos, 10,034 images and 123,751 bounding box annotations for 17 classes of PPE adherence and nonadherence for eyewear, masks, gowns, and gloves, and one additional head class. Evaluations using newly proposed metrics confirm R2PPE exhibits higher annotation density than three established general-purpose and medical PPE datasets. The R2PPE dataset provides a resource for developing computer vision algorithms for monitoring PPE use in healthcare.https://doi.org/10.1038/s41597-024-04355-0 |
| spellingShingle | Wanzhao Yang Mary S. Kim Genevieve J. Sippel Aaron H. Mun Kathleen H. McCarthy Beomseok Park Aleksandra Sarcevic Marius George Linguraru Ivan Marsic Randall S. Burd An image dataset for surveillance of personal protective equipment adherence in healthcare Scientific Data |
| title | An image dataset for surveillance of personal protective equipment adherence in healthcare |
| title_full | An image dataset for surveillance of personal protective equipment adherence in healthcare |
| title_fullStr | An image dataset for surveillance of personal protective equipment adherence in healthcare |
| title_full_unstemmed | An image dataset for surveillance of personal protective equipment adherence in healthcare |
| title_short | An image dataset for surveillance of personal protective equipment adherence in healthcare |
| title_sort | image dataset for surveillance of personal protective equipment adherence in healthcare |
| url | https://doi.org/10.1038/s41597-024-04355-0 |
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