Innovative Cybersickness Detection: Exploring Head Movement Patterns in Virtual Reality
Date
2024-02-05
Authors
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
arXiv
Abstract
Despite the widespread adoption of Virtual Reality (VR) technology, cybersickness remains a barrier for some users. This research investigates head movement patterns as a novel physiological marker for cybersickness detection. Unlike traditional markers, head movements provide a continuous, non-invasive measure that can be easily captured through the sensors embedded in all commercial VR headsets. We used a publicly available dataset from a VR experiment involving 75 participants and analyzed head movements across six axes. An extensive feature extraction process was then performed on the head movement dataset and its derivatives, including velocity, acceleration, and jerk. Three categories of features were extracted, encompassing statistical, temporal, and spectral features. Subsequently, we employed the Recursive Feature Elimination method to select the most important and effective features. In a series of experiments, we trained a variety of machine learning algorithms. The results demonstrate a 76% accuracy and 83% precision in predicting cybersickness in the subjects based on the head movements. This study contribution to the cybersickness literature lies in offering a preliminary analysis of a new source of data and providing insight into the relationship of head movements and cybersickness.
Series Number
Journal Issue
Is Version Of
Versions
Series
Academic or Administrative Unit
Type
Preprint
Comments
This is a preprint from Salehi, Masoud, Nikoo Javadpour, Brietta Beisner, Mohammadamin Sanaei, and Stephen B. Gilbert. "Innovative Cybersickness Detection: Exploring Head Movement Patterns in Virtual Reality." arXiv preprint arXiv:2402.02725 (2024). doi: https://doi.org/10.48550/arXiv.2402.02725. Copyright 2024 The Authors. CC BY.