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Updated: Jul 30, 2025

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Study of Motion Sickness Model Based on fNIRS Multiband Features during Car Rides.

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  • 1Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China.

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Summary

Functional near-infrared spectroscopy (fNIRS) effectively models motion sickness by analyzing prefrontal cortex blood oxygenation changes. This research achieved 87.3% classification accuracy, highlighting fNIRS potential for understanding motion sickness.

Keywords:
functional near-infrared spectroscopy (fNIRS)motion sicknessmotion sickness modelpower spectral entropy (PSE)principal component analysis (PCA)support vector machine (SVM)wavelet decomposition

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Area of Science:

  • Neuroscience
  • Biomedical Engineering

Background:

  • Motion sickness is a prevalent issue during travel, impacting passenger comfort and experience.
  • Understanding the physiological underpinnings of motion sickness is crucial for developing effective countermeasures.

Purpose of the Study:

  • To investigate the relationship between prefrontal cortex blood oxygenation changes and motion sickness severity using functional near-infrared spectroscopy (fNIRS).
  • To develop a classification model for motion sickness based on neuroimaging data.

Main Methods:

  • Real-world vehicle testing employing fNIRS to measure cerebral blood oxygenation.
  • Principal Component Analysis (PCA) for feature extraction and Wavelet decomposition for Power Spectrum Entropy (PSE) analysis.
  • Support Vector Machine (SVM) model for classifying motion sickness severity based on fNIRS data.

Main Results:

  • The study achieved an overall motion sickness classification accuracy of 87.3% using 78 datasets.
  • Analysis revealed a strong correlation between motion sickness magnitude and changes in the PSE of prefrontal blood oxygenation across five frequency bands.
  • Individual classification accuracy varied significantly (50%-100%) among the 13 subjects, indicating personal differences.

Conclusions:

  • fNIRS is a viable technique for modeling and classifying motion sickness by assessing prefrontal cortex activity.
  • Changes in cerebral blood oxygenation, specifically PSE, are closely linked to the severity of motion sickness.
  • Further research is necessary to address the observed individual variability in motion sickness responses.