Open Access

ARTICLE

An Automated Detection Approach of Protective Equipment Donning for Medical Staff under COVID-19 Using Deep Learning

by Qiang Zhang¹, Ziyu Pei¹, Rong Guo¹, Haojun Zhang², Wanru Kong², Jie Lu³, Xueyan Liu^1,*

1 Department of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China
2 Gansu Provincial People's Hospital, Lanzhou, 730070, China
3 Health Statistics and Information Center of Gansu Province, Health Commission of Gansu Province, Lanzhou, 730070, China

* Corresponding Author: Xueyan Liu. Email:

(This article belongs to the Special Issue: Paradigms of Deep Learning, Big Data Analytics, Artificial Intelligence and Mathematical Statistics in Medical Applications for Combating Epidemics)

Computer Modeling in Engineering & Sciences 2022, 132(3), 845-863. https://doi.org/10.32604/cmes.2022.019085

Received 02 September 2021; Accepted 25 January 2022; Issue published 27 June 2022

Abstract

Personal protective equipment (PPE) donning detection for medical staff is a key link of medical operation safety guarantee and is of great significance to combat COVID-19. However, the lack of dedicated datasets makes the scarce research on intelligence monitoring of workers’ PPE use in the field of healthcare. In this paper, we construct a dress codes dataset for medical staff under the epidemic. And based on this, we propose a PPE donning automatic detection approach using deep learning. With the participation of health care personnel, we organize 6 volunteers dressed in different combinations of PPE to simulate more dress situations in the preset structured environment, and an effective and robust dataset is constructed with a total of 5233 preprocessed images. Starting from the task's dual requirements for speed and accuracy, we use the YOLOv4 convolutional neural network as our learning model to judge whether the donning of different PPE classes corresponds to the body parts of the medical staff meets the dress codes to ensure their self-protection safety. Experimental results show that compared with three typical deep-learning-based detection models, our method achieves a relatively optimal balance while ensuring high detection accuracy (84.14%), with faster processing time (42.02 ms) after the average analysis of 17 classes of PPE donning situation. Overall, this research focuses on the automatic detection of worker safety protection for the first time in healthcare, which will help to improve its technical level of risk management and the ability to respond to potentially hazardous events.

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