DiVRsify: Break the Cycle and Develop VR for Everyone

10/01/2021
by   Tabitha C. Peck, et al.
0

Virtual reality technology is biased. It excludes approximately 95 world's population by being primarily designed for male, western, educated, industrial, rich, and democratic populations. This bias may be due to the lack of diversity in virtual reality researchers, research participants, developers, and end users, fueling a noninclusive research, development, and usability cycle. The objective of this paper is to highlight the minimal virtual reality research involving understudied populations with respect to dimensions of diversity, such as gender, race, culture, ethnicity, age, disability, and neurodivergence. Specifically, we highlight numerous differences in virtual reality usability between underrepresented groups compared to commonly studied populations. These differences illustrate the lack of generalizability of prior virtual reality research. Lastly, we present a call to action with the aim that, over time, will break the cycle and enable virtual reality for everyone.

READ FULL TEXT
research
02/02/2020

Non-Euclidean Virtual Reality IV: Sol

This article presents virtual reality software designed to explore the S...
research
09/09/2021

Rethinking Immersive Virtual Reality and Empathy

In this position paper, we aim to spark more discussions surrounding the...
research
10/02/2019

Brown Ring Experiment in Virtual Reality

Brown Ring Experiment is a very popular test to detect the presence of N...
research
06/12/2021

Metrics for 3D Object Pointing and Manipulation in Virtual Reality

Assessing the performance of human movements during teleoperation and vi...
research
02/08/2023

SURVIVRS: Surround Video-Based Virtual Reality for Surgery Guidance

There is a strong demand for virtual reality (VR) to bring quality healt...
research
03/09/2022

Immersive Virtual Reality Simulations of Bionic Vision

Bionic vision uses neuroprostheses to restore useful vision to people li...
research
05/24/2023

A Virtual Reality Tool for Representing, Visualizing and Updating Deep Learning Models

Deep learning is ubiquitous, but its lack of transparency limits its imp...

Please sign up or login with your details

Forgot password? Click here to reset