Sick Moves! Motion Parameters as Indicators of Simulator Sickness

Feigl T, Roth D, Gradl S, Wirth M, Latoschik ME, Eskofier B, Philippsen M, Mutschler C (2019)

Publication Language: English

Publication Type: Journal article, Original article

Publication year: 2019

Journal

IEEE Transactions on Visualization and Computer Graphics Institute of Electrical and Electronics Engineers (IEEE)

Book Volume: 25

Pages Range: 3146-3157

Article Number: 8798880

Journal Issue: 11

URI: https://ieeexplore.ieee.org/document/8798880

DOI: 10.1109/TVCG.2019.2932224

Open Access Link: https://ieeexplore.ieee.org/document/8798880

Abstract

We explore motion parameters, more specifically gait parameters, as an objective indicator to assess simulator sickness in Virtual Reality (VR). We discuss the potential relationships between simulator sickness, immersion, and presence. We used two different camera pose (position and orientation) estimation methods for the evaluation of motion tasks in a large-scale VR environment: a simple model and an optimized model that allows for a more accurate and natural mapping of human senses. Participants performed multiple motion tasks (walking, balancing, running) in three conditions: a physical reality baseline condition, a VR condition with the simple model, and a VR condition with the optimized model. We compared these conditions with regard to the resulting sickness and gait, as well as the perceived presence in the VR conditions. The subjective measures confirmed that the optimized pose estimation model reduces simulator sickness and increases the perceived presence. The results further show that both models affect the gait parameters and simulator sickness, which is why we further investigated a classification approach that deals with non-linear correlation dependencies between gait parameters and simulator sickness. We argue that our approach could be used to assess and predict simulator sickness based on human gait parameters and we provide implications for future research.

Authors with CRIS profile

Tobias Feigl Lehrstuhl für Informatik 2 (Programmiersysteme) Daniel Roth
Stefan Gradl-Trautvetter Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Markus Wirth Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Björn Eskofier Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Michael Philippsen Lehrstuhl für Informatik 2 (Programmiersysteme) Christopher Mutschler Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics)

Additional Organisation(s)

Machine Learning and Data Analytics Lab

Related research project(s)

Recurrent Neuronal Networks (RNNs) for Real-Time Estimation of Nonlinear Motion Models (RuNN) Oct. 1, 2017 - March 31, 2021

Involved external institutions

Julius-Maximilians-Universität Würzburg DE Germany (DE)

How to cite

APA:

Feigl, T., Roth, D., Gradl, S., Wirth, M., Latoschik, M.E., Eskofier, B.,... Mutschler, C. (2019). Sick Moves! Motion Parameters as Indicators of Simulator Sickness. IEEE Transactions on Visualization and Computer Graphics, 25(11), 3146-3157. https://dx.doi.org/10.1109/TVCG.2019.2932224

MLA:

Feigl, Tobias, et al. "Sick Moves! Motion Parameters as Indicators of Simulator Sickness." IEEE Transactions on Visualization and Computer Graphics 25.11 (2019): 3146-3157.

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