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Related Experiment Video

Updated: Sep 29, 2025

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Detecting and predicting visually induced motion sickness with physiological measures in combination with machine

Behrang Keshavarz1, Katlyn Peck2, Sia Rezaei2

  • 1KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, Canada; Ryerson University, Department of Psychology, Toronto, Canada.

International Journal of Psychophysiology : Official Journal of the International Organization of Psychophysiology
|March 20, 2022
PubMed
Summary

Machine learning and physiological data show potential for detecting visually induced motion sickness (VIMS). However, these methods are not yet reliable for real-time VIMS severity prediction.

Keywords:
ECGPosturePsychophysiologyRandom ForestSimulator sicknessTemperature

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Area of Science:

  • Human-Computer Interaction
  • Neuroscience
  • Biomedical Engineering

Background:

  • Visually induced motion sickness (VIMS) is a prevalent issue with virtual reality and digital displays.
  • Objective, real-time VIMS detection remains a significant challenge in human-computer interaction.

Purpose of the Study:

  • To explore the efficacy of machine learning (ML) combined with physiological measures for real-time VIMS detection and severity prediction.
  • To assess the correlation between various physiological signals and subjective VIMS intensity.

Main Methods:

  • Forty-three healthy adults were exposed to a VIMS-inducing video.
  • Physiological data (ECG, EDA, EGG, respiration, temperature, body movements) were collected.
  • Machine learning models analyzed physiological changes against subjective VIMS scores (FMS, SSQ).

Main Results:

  • 72% of participants experienced VIMS.
  • Facial skin temperature and body movements demonstrated the strongest correlation with VIMS.
  • ML models showed medium correlation with real-time VIMS severity and acceptable classification of sick vs. non-sick states.

Conclusions:

  • Physiological measures show promise for VIMS assessment.
  • Current physiological and ML approaches are insufficient as standalone methods for reliable real-time VIMS severity detection or prediction.