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Physiological data-driven models for motion sickness prediction
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DOI：10.1016/j.apergo.2026.104739.png)
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Abstract
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• Motion sickness binary classification using physiological data achieves 81% accuracy. • Electrodermal activity and neck surface electromyography data are suitable indicators for MS prediction. • Self-reported MS scores can lag MS-triggering head motion exposure by 180 s.
Keywords:
Machine learning
Heart rate
Surface electromyographic data
Motion sickness
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