Integrated Simulation and Assessment in Donning and Doffing for Healthcare Professionals (ISADD)

EasyChair Preprint 7325

Abstract

There is an unprecedented increase in the requirement for healthcare staff trained in infection control principles due to the COVID-19 pandemic. This paper presents an example of a technological solution to correctly explaining the sequence of donning and doffing Personal Protective Equipment (PPE) in preparation for dealing with patients who may be infected with a virus. Image processing and machine learning techniques are used to identify articles of PPE as they are worn, and an innovative client server architecture is used to mitigate performance issues given the need for near real time response (NRT). A powerful inference server is used to identify PPE and human activity, while the donning/doffing sequence is monitored by the client application. If the user does not follow the correct steps, the user is notified regarding the missed step in the sequence and has the option to begin the process again. A technical evaluation of the effectiveness of the identification of PPE based on image processing gives a technical insight into how correctly image processing is being carried out, while a qualitative evaluation of the donning and doffing process with qualified infection control staff, as well as nurse trainees is also being carried out to gain an understanding of the possible acceptance of the application.

Keyphrases: Ed-Tech, Healthcare, health tech, image processing, machine learning

@booklet{EasyChair:7325,
  author    = {Neville Schembri and Daren Scerri and Gerard Said Pullicino and Dorianne Cachia and Phyllis Farrugia Abanfi and Jonathan Vella and Lorna Bonnici West},
  title     = {Integrated Simulation and Assessment in Donning and Doffing for Healthcare Professionals (ISADD)},
  howpublished = {EasyChair Preprint 7325},
  year      = {EasyChair, 2022}}