Study of Cybersickness Prediction in Real Time Using Eye Tracking Data

Cybersickness seriously degrades users' experiences of virtual real-ity (VR). The level of cybersickness is commonly gauged through a simulator sickness questionnaire (SSQ) administered after the expe-rience. However, for observing the user's health and evaluating the VR content/device, me...

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Bibliographic Details
Published in2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) pp. 871 - 872
Main Authors Shimada, Shogo, Ikei, Yasushi, Nishiuchi, Nobuyuki, Yem, Vibol
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2023
Subjects
Cybersickness
eye tracking
Gaze tracking
Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Virtual reality
Machine learning
real-time prediction
Real-time systems
Three-dimensional displays
Time series analysis
User interfaces
virtual environments
VR sickness
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Summary:Cybersickness seriously degrades users' experiences of virtual real-ity (VR). The level of cybersickness is commonly gauged through a simulator sickness questionnaire (SSQ) administered after the expe-rience. However, for observing the user's health and evaluating the VR content/device, measuring the level of cybersickness in real time is essential. In this study, we examined the relationship between eye tracking data and sickness level, then predicted the sickness level using machine learning methods. Some characteristics of eye related indices significantly differed between the sickness and non-sickness groups. The machine learning methods predicted cybersickness in real time with an accuracy of approximately 70%.
DOI:10.1109/VRW58643.2023.00278