Identifying presence of cybersickness symptoms using AI-based predictive learning algorithms

Syed Fawad M. Zaidi, Niusha Shafiabady, Justin Beilby

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
JournalVirtual Reality
DOIs
Publication statusPublished - 2023

Keywords

  • Artificial intelligence (AI)
  • Cybersickness (CS)
  • Decision trees (DTs)
  • Head-mounted displays (HMDs)
  • K-nearest neighbours (KNNs)
  • Machine learning (ML)
  • Support vector machines (SVMs)
  • Virtual reality (VR)

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