Construction of a computational model of individual progression of motion sickness symptoms based on subjective vertical conflict theory.

Abstract

Computational models predicting motion sickness have advanced, particularly those based on subjective vertical conflict (SVC) theory. While SVC-based models primarily predict motion sickness incidence (MSI), which is defined as the percentage of people who would vomit under a given motion, models predicting milder individual symptoms, which are crucial for daily applications, are still required. Recently, computational models predicting vestibular motion-sickness progression using the SVC theory have been developed by changing the output of a 6DOF-SVC model from MSI to the Misery Scale (MISC), a subjective measure of symptom progression. In practical applications, the ability to predict MISC for unseen motions is crucial. The present study conceived a method for predicting MISC beyond a certain point in the future by identifying parameters from data collected up to that point. Therefore, this study investigates the effect of the number of data points used for parameter identification on the future prediction accuracy. Observed MISC responses from participants exposed to linear lateral motion in darkness were used for model validation. The results indicated that prediction accuracy increased as more data points were included. On average, using more than 5-10 min of data significantly increased the accuracy compared to a model using averaged parameter sets across participants, although the tendency significantly differed based on an individual's MISC history. A trial considering individual MISC histories, in which data points were defined when the observed MISC first reached certain levels, showed a general trend of improved accuracy when data up to MISC Level 3 was used. The findings of this study demonstrate that motion sickness symptom progression can be predicted with reduced error by incorporating individual symptom histories, thereby providing a foundation for the development of personalized motion sickness prediction models applicable to broader applications.