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The existing computational models used to estimate motion sickness areincapable of describing the fact that the predictability of motion patternsaffects motion sickness. Therefore, the present study proposes a computationalmodel to describe the effect of the predictability of dynamics or the patternof motion stimuli on motion sickness. In the proposed model, a submodel, inwhich a recursive Gaussian process regression is used to represent humanfeatures of online learning and future prediction of motion dynamics, iscombined with a conventional model of motion sickness based on an observertheory. A simulation experiment was conducted in which the proposed modelpredicted motion sickness caused by a 900 s horizontal movement. The movementwas composed of a 9 m repetitive back-and-forth movement pattern with a pause.Regarding the motion condition, the direction and timing of the motion werevaried as follows: a) Predictable motion (M_P): the direction of the motion andduration of the pause were set to 8 s; b) Motion with unpredicted direction(M_dU): the pause duration was fixed as in (P), but the motion direction wasrandomly determined; c) Motion with unpredicted timing (M_tU): the motiondirection was fixed as in (M_P), but the pause duration was randomly selectedfrom 4 to 12 s. The results obtained using the proposed model demonstrated thatthe predicted motion sickness incidence for (M_P) was smaller than those for(M_dU) and (M_tU). This tendency agrees with the sickness patterns observed ina previous experimental study in which the human participants were subject tomotion conditions similar to those used in our simulations. Moreover, nosignificant differences were found in the predicted motion sickness incidencesat different conditions when the conventional model was used.

Computational Model of Motion Sickness Describing the Effects of Learning Exogenous Motion Dynamics