Computer Graphics
TU Braunschweig

Exploring Neural and Peripheral Physiological Correlates of Simulator Sickness

Exploring Neural and Peripheral Physiological Correlates of Simulator Sickness

This paper investigates neural and physiological correlates of simulator sickness through a controlled experiment conducted within a fully immersive dome projection system. Our goal is to establish a reliable, objective and in-situ measurable predictive indicator of simulator sickness.

Simulator sickness is a problem common to all types of visual simulators consisting of motion sickness-like symptoms that may be experienced while and after being exposed to a dynamic, immersive visualization. It leads to ethical concerns and impaired validity of simulator-based research. Due to the popularity of virtual reality devices, the number of people exposed to this problem is increasing and, therefore, it is crucial to find reliable predictors of this condition before any symptoms appear.

Despite its relevance and the several theories about its origins, simulator sickness cannot yet be quantitatively modelled and predicted. Our results indicate that, while neural correlates did not materialize, physiological measures may be a solid early indicator of oncoming simulator sickness.

Author(s):Jan-Philipp Tauscher, Alexandra Witt, Sebastian Bosse, Fabian Wolf Schottky, Steve Grogorick, Susana Castillo, Marcus Magnor
Published:August 2020
Journal:Computer Animation and Virtual Worlds Vol. n/a
Presented at:International Conference on Computer Animation and Social Agents (CASA) 2020
Note:electronic ISSN: 1546-427X
Project(s): ElectroEncephaloGraphics  Immersive Digital Reality  ICG Dome 

  title = {Exploring Neural and Peripheral Physiological Correlates of Simulator Sickness},
  author = {Tauscher, Jan-Philipp and Witt, Alexandra and Bosse, Sebastian and Schottky, Fabian Wolf and Grogorick, Steve and Castillo, Susana  and Magnor, Marcus},
  journal = {Computer Animation and Virtual Worlds},
  doi = {10.1002/cav.1953},
  volume = {n/a},
  number = {n/a},
  note = {electronic {ISSN}: 1546-427X},
  pages = {e1953 ff.},
  month = {Aug},
  year = {2020}