CyberSense: A Closed-Loop Framework to Detect Cybersickness Severity and Adaptively apply Reduction Techniques

Researchers often collect subjective measurements before, after, and during the immersive experience to measure cybersickness severity. However, collecting data before and after the immersive experience does not provide a sufficiently granular understanding of cybersickness during the immersion. Thu...

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Bibliographic Details
Published in2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) pp. 148 - 155
Main Authors Islam, Rifatul, Ang, Samuel, Quarles, John
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2021
Subjects
automated cybersickness detection and reduction
Conferences
Cybersickness
Human computer interaction (HCI)
Human-centered computing
Immersive experience
Interaction paradigms
Neural networks
Physiology
Three-dimensional displays
User interfaces
Virtual reality
visually induced motion sickness
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DOI10.1109/VRW52623.2021.00035

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Summary:Researchers often collect subjective measurements before, after, and during the immersive experience to measure cybersickness severity. However, collecting data before and after the immersive experience does not provide a sufficiently granular understanding of cybersickness during the immersion. Thus, no preventive measures can be taken during immersion. This research presents CyberSense - an automated framework for cybersickness severity detection during immersion. The framework collects the users' physiological data at user-defined intervals. It uses a pre-trained neural network to detect cybersickness severity on the experience with a root mean square error of 2.61 and adaptively adjusts the cybersickness reduction techniques.
DOI:10.1109/VRW52623.2021.00035