Loading...
Identifying presence of cybersickness symptoms using AI-based predictive learning algorithms
Zaidi, Syed Fawad M. Shafiabady, Niusha Beilby, Justin
Zaidi, Syed Fawad M.
Shafiabady, Niusha
Beilby, Justin
Abstract
Cybersickness (CS) affects a large proportion of virtual reality (VR) users causing a combination of nausea, headaches and dizziness which would create barriers to the users, VR designers/developers and the stakeholders in the production industry. Although design principles suggest methods to avoid CS, challenges remain as new demands and systems continue to penetrate the competitive market. The dilemma is whether to use VR technology by experiencing the ultimate virtual world using a head-mounted display (HMD) with possible CS triggers or to avoid the triggers by avoiding using VR. With the huge success and potential in the entertainment industry, it is very important to focus on the solutions to handling CS dilemmas. Therefore, the main observation for the developers is to have a guide around the set of established design principles aiming to broadly reduce CS. In this paper, we provide a method to apply artificial intelligence (AI) techniques and use machine learning (ML) algorithms including support vector machines (SVMs), decision trees (DTs) and K-nearest neighbours (KNNs) to predict CS outcomes. Based on our findings, we have observed that DT and SVM surpassed KNN in test accuracy. Additionally, DT exhibited better results than both SVM and KNN in train accuracy. By exploiting the power of ML, developers will be able to predict the potential occurrence of CS while developing VR projects to find ways to alleviate CS more effectively.
Keywords
Cybersickness (CS), Virtual reality (VR), Head-mounted displays (HMDs), achine learning (ML), Artificial intelligence (AI), Support vector machines (SVMs), Decision trees (DTs), K-nearest neighbours (KNNs)
Date
2023
Type
Journal article
Journal
Virtual Reality
Book
Volume
27
Issue
4
Page Range
3613-3620
Article Number
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business
Collections
Relation URI
Source URL
Event URL
Open Access Status
Published as ‘gold’ (paid) open access
License
CC BY 4.0
File Access
Open
Notes
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.