Authors: Rifatul Islam, Samuel Ang, John Quarles.

Published ath the 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)

Abstract:

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.

1. Run The Sensor API
2. Run the CyberSense Server
3. Run RollerCoaster Example

@INPROCEEDINGS {9419297, author={Islam, Rifatul and Ang, Samuel and Quarles, John}, booktitle={2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)}, title={CyberSense: A Closed-Loop Framework to Detect Cybersickness Severity and Adaptively apply Reduction Techniques}, year={2021}, volume={}, number={}, pages={148-155}, doi={10.1109/VRW52623.2021.00035}}