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

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 845-863, 2022, DOI:10.32604/cmes.2022.019085

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… More >

Long Liu^1,2, Yanlin Shi^1,*, Xiyan Fei³, Zhenzhen Wang^1,4, Zhi Wang^1,5, Li Li⁶, Lin Ding⁶, Qiaoyuan Yan^7,*

International Journal of Mental Health Promotion, Vol.23, No.3, pp. 319-330, 2021, DOI:10.32604/IJMHP.2021.015699

Abstract Exploring whether medical staff perceive stress on the assigned medical tasks, what are the specific sources of stress, what are the tangible sources of support they expected to be helpful, and individual coping with stress to provide more accurate, personal support for psychological crisis. This study uses a cross-sectional descriptive survey adopting convenience sampling among the medical staff who worked for over seven days in the infected areas of one Grade 2A and three Grade 3A hospitals during the COVID-19 pandemic. The assessment includes attitude when receiving tasks, major stressors, factors relieving stress, and personal management of stress. A total… More >