Predicting Motion Sickness Sensitivity

Objective To determine the combined effectiveness of personal factors in predicting motion sickness sensitivity elicited by linear motion of a sled. Background Technologies that create challenging motion environments have spurred the development of mathematical models aimed at predicting motion sickness. Including personal factors may improve their predictive accuracy. History of motion sickness, age, gender, and visual dependence have been identified as potential predictors, although their individual predictive value is limited. Method We assessed motion sickness history, age, and gender through a questionnaire. To evaluate visual dependence, participants oriented a virtual rod parallel to the orientation of gravity while observing a tilted static frame or a disc rotating in roll. Sensitivity is expressed using a new measure that considers both the magnitude and accumulation of self-reported symptoms. Results Our results confirmed the predictive value of history and showed a small yet significant contribution of dynamic visual dependence, with greater dependence associated with higher sensitivity ( R 2 = 25%). Recent history reported over the past 10 years was demonstrated as a better predictor than lifetime history. Age, gender, and static visual dependence did not enhance the prediction. Conclusion Combining multiple predictors shows little advantage for improving a prediction of sensitivity beyond history alone. Future studies could confirm the predictive value of dynamic visual dependence and seek to optimize recent history, for example, by restricting time period and motion environment. Application Improved prediction models may support tailoring system parameters to user sensitivity in technologies such as automated driving, head mounted displays, and simulators.