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Identification of the normative use of medical protective equipment by fusion of object detection and keypoints detection.
Pei, Ziyu; Zhang, Qiang; Qi, Ying; Wen, Zexin; Zhang, Zheng.
Afiliación
  • Pei Z; Department of Computer Science and Engineering, Northwest Normal University, #967 Anning east RD, Lanzhou, Gansu Province 730070, China.
  • Zhang Q; Department of Computer Science and Engineering, Northwest Normal University, #967 Anning east RD, Lanzhou, Gansu Province 730070, China. Electronic address: zhangq@nwnu.edu.cn.
  • Qi Y; Department of Computer Science and Engineering, Northwest Normal University, #967 Anning east RD, Lanzhou, Gansu Province 730070, China.
  • Wen Z; Department of Computer Science and Engineering, Northwest Normal University, #967 Anning east RD, Lanzhou, Gansu Province 730070, China.
  • Zhang Z; Department of Computer Science and Engineering, Northwest Normal University, #967 Anning east RD, Lanzhou, Gansu Province 730070, China.
Comput Methods Programs Biomed ; 244: 107972, 2024 Feb.
Article en En | MEDLINE | ID: mdl-38070391
BACKGROUND AND OBJECTIVE: Irregular use of personal protective equipment (PPE) seriously affects the occupational health and safety of healthcare workers, especially in large public health emergencies such as COVID-19. The danger often occurs in some complex scenarios where the use of certain equipment does not comply with the spatial rules it specifies. The single object detection-based method is difficult to effectively verify whether the worker's use of PPE is normative. Also detecting the use of six classes of PPE brings a large computational load to the task. METHODS: In this paper, we proposed an identification approach that combined human keypoints detection with deep learning object detection to help facilitate the monitoring of healthcare workers' standard PPE use. We used YOLOv4 as the baseline model for PPE detection and MobileNetv3 as the backbone of the detector to reduce the computational effort. In addition, High-Resolution Net (HRNet) was the benchmark for keypoints detection, characterizing the coordinates of 25 key points in the human body. Generalized Intersection over Union (GIoU) was used to establish the association between PPEs and key points, and by calculating the matching score between a PPE and the corresponding body bounding boxes, the authenticity of the PPE specification could be effectively inferred. RESULTS: The proposed detector is able to identify whether a healthcare worker is normatively using multiple equipment with a higher precision (95.81 %), recall (96.38 %), and F1-score (96.09 %). Meanwhile, the number of parameters (2.87 M) and the size of the model (6.4 MB) are also more lightweight than other comparative detectors. CONCLUSIONS: Our approach is more reliable for reasoning about the normality of personal protection for healthcare workers in some complex scenarios than a single object detection-based approach. The developed identification framework provides a new automated monitoring solution for protection management in healthcare, and the modular design brings more flexible applications for different medical operation scenarios.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 1_ASSA2030 / 2_ODS3 / 4_TD Problema de salud: 1_doencas_nao_transmissiveis / 2_muertes_prematuras_enfermedades_notrasmisibles / 4_pneumonia Asunto principal: COVID-19 Límite: Humans Idioma: En Revista: Comput Methods Programs Biomed Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 1_ASSA2030 / 2_ODS3 / 4_TD Problema de salud: 1_doencas_nao_transmissiveis / 2_muertes_prematuras_enfermedades_notrasmisibles / 4_pneumonia Asunto principal: COVID-19 Límite: Humans Idioma: En Revista: Comput Methods Programs Biomed Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: China
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