Design of a Human Centered Computing (HCC) based Virtual Reality Simulator to train First Responders Involved in the COVID-19 Pandemic

Abstract

The COVID-19 pandemic has placed an overwhelming strain on our Nation's ability to treat patients; the number of patients who need to be tested continues to rise. With nurses also becoming infected, the number of trained professionals who can perform tasks such as testing of patients along with providing care involving hooking up patients to ventilators continues to decrease as well. There is a need to explore the adoption of virtual computer-based training mediums which will enable new nurses and others to be trained in safe and efficient procedures involving patients during this pandemic period. In this paper, the design of a VR-based simulator based on Human-Centered Computing (HCC) principles are discussed. The role of HCC factors such as affordance and cognitive load on the comprehension and scene understanding of nurses during training and the acquisition of knowledge of safety procedures and detailed steps (pertaining to nasal sample collection and use of ventilators on patients) has been studied with the involvement of nurse and nurse trainee participants. Adoption of a participatory design approach involving experts (nurses, doctors involved in covid-19 testing and treatment) has provided a foundational basis for the design of the training environments and assessment activities. Formal information-centric process models of the nasal swabbing procedures and ventilator hookup tasks were created using the engineering Enterprise Modeling Language (eEML). The preliminary results from the assessment activities indicate the positive impact of such HCC based 3D simulators in such training of first responders.

Virtual Reality Applications in Medicine During the COVID-19 Pandemic: Systematic Review

“…First, an immersive VR simulation (ie, COVID-19 VR Strikes Back) was at least as effective as traditional learning methods for training medical students regarding COVID-19–related skills [ 68 ]. Second, 2 immersive VR experiences, one involving wearing and stripping personal protective equipment [ 73 ] and the other involving the management of patients with respiratory infectious diseases due to COVID-19 [ 77 ], provided an effective and safe alternative to training nurses during the first year of the COVID-19 pandemic. Third, a virtual simulation of 2 patients (ie, COVID-19 and surgical trauma), which tried using desktop displays, helped nursing students bridge gaps in teaching and learning processes [ 104 ].…”

“…This technology has been successfully used for teaching various topics, including neuroanatomy and clinical anatomy (eg, [ 84 , 93 ]). VR has also been adopted to train COVID-19–related skills among doctors and nurses [ 68 , 73 ]. Studies that emerged from this systematic review showed that, where available, high-fidelity immersive VR was adopted heavily after the outbreak of the COVID-19 pandemic [ 88 , 103 ].…”

“…Various types of HMDs (ie, PC-based, mobile, and standalone) and contents (ie, 360° videos and photos, virtual environments, VR games, and embodied virtual agents) have been used with positive results in medicine during the COVID-19 crisis (eg, [ 48 , 73 , 101 ]). This underscores how nowadays it is possible to choose among different VR hardware and software.…”

“…They should offer clear guidelines for correctly using this technology in mental health, and medical education and training [15]. Second, when the graphic quality of the virtual experience was perceived as too low, users had difficulties comprehending a target scenario [73,87]. To prevent graphical or technical problems (eg, breakdown or problems with interaction) [139,140], with potential detrimental effects on treatment and learning outcomes [137,141], the principles and practices of user-centered design are recommended [142,143].…”

The human experience of comprehending source code in virtual reality

“…The NASA TLX survey is a multi-dimensional questionnaire designed to determine the task load of a participant performing a task (Hart and Staveland 1988;Hart 2006). Various research communities have adopted it as a reliable measure of task load (Afridi and Mengash 2020;Cecil et al 2021). Said et al demonstrated that a modified NASA TLX survey accurately reflects the assumed influences of various covariates on perceived workload (Said et al 2020).…”