Authors:
Johannes Lindner, Markus Böckle, Mathias Pechinger, Klaus Bogenberger
Keywords:
simulator sickness, Cybersickness, virtual reality, bicycle simulator, Dropout Prediction, Survival Analysis
Abstract:
Lindner J.; Böckle M.; Pechinger M. and Bogenberger K. Introducing Surrogate Measures for Objective Simulator Sickness Evaluation In: Proceedings of the Driving Simulation Conference 2025 Europe XR, Driving Simulation Association, Stuttgart, Germany, 2025, pp. 87-94
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@inproceedings{Lindner2025,
title = {Introducing Surrogate Measures for Objective Simulator Sickness Evaluation},
author = {Johannes Lindner and Markus Böckle and Mathias Pechinger and Klaus Bogenberger
},
editor = {Andras Kemeny and Jean-Rémy Chardonnet and Florent Colombet and Stéphane Espié},
doi = {https://doi.org/10.82157/dsa/2025/10},
isbn = {978-2-9573777-7-0},
year = {2025},
date = {2025-09-24},
booktitle = {Proceedings of the Driving Simulation Conference 2025 Europe XR},
pages = {87-94},
address = {Stuttgart, Germany},
organization = {Driving Simulation Association},
abstract = {Simulator sickness poses a significant challenge in human-in-the-loop studies, exhibiting high variability depending on the specific simulator setup and study scenario. Traditional reliance on subjective measurement methods hinders objective comparison between studies and complicates the prediction of participant dropout. This paper introduces a methodology to objectively predict simulator sickness related study dropout using quantifiable surrogate measures derived directly from simulator data. We conducted a study with 50 participants using a CAVE-based virtual reality bicycle simulator. Surrogate measures, including cumulative speed change and cumulative angular change over exposure time, were defined. Dropout probability was analyzed using Kaplan-Meier estimation, and predictors were identified via Cox proportional hazards regression. Results indicated that higher cumulative speed change was significantly associated with increased dropout rate. Cumulative angular change, age, and gender, were not found to be significant predictors in the tested models. This approach demonstrates a robust methodology for creating simulator-specific dropout models, facilitating more reliable study planning.
},
keywords = {},
}
Download .bib file
TY - CONF
TI - Introducing Surrogate Measures for Objective Simulator Sickness Evaluation
AU - Lindner, Johannes
AU - Böckle, Markus
AU - Pechinger, Mathias
AU - Bogenberger, Klaus
C1 - Stuttgart, Germany
C3 - Proceedings of the Driving Simulation Conference 2025 Europe XR
DA - 2025/09/24
PY - 2025
SP - 87
EP - 94
LA - en-US
PB - Driving Simulation Association
SN - 978-2-9573777-7-0
L2 - https://proceedings.driving-simulation.org/proceeding/dsc-2025/introducing-surrogate-measures-for-objective-simulator-sickness-evaluation
ER -
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