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

Abstract

The existing computational models used to estimate motion sickness are incapable of describing the fact that the predictability of motion patterns affects motion sickness. Therefore, the present study proposes a computational model to describe the effect of the predictability of dynamics or the pattern of motion stimuli on motion sickness. In the proposed model, a submodel - in which a recursive Gaussian process regression is used to represent human features of online learning and future prediction of motion dynamics - is combined with a conventional model of motion sickness based on an observer theory. A simulation experiment was conducted in which the proposed model predicted motion sickness caused by a 900 s horizontal movement. The movement was 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 were varied as follows: (a) Predictable motion (M_P): the direction of the motion and duration of the pause were set to 8 s; (b) Motion with unpredicted direction (M_dU): the pause duration was fixed as in (M_P), but the motion direction was randomly determined; (c) Motion with unpredicted timing (M_tU): the motion direction was fixed as in (M_P), but the pause duration was randomly selected from 4 to 12 s. The results obtained using the proposed model demonstrated that the predicted motion sickness incidence for (M_P) was smaller than those for (M_dU) and (M_tU) and no considerable difference was found between M_dU and M_tU. This tendency agrees with the sickness patterns observed in a previous experimental study in which the human participants were subject to motion conditions similar to those used in our simulations. Moreover, no significant differences were found in the predicted motion sickness incidences at different conditions when the conventional model was used.

Keywords: computational model; learning; motion dynamics; motion pattern; motion sickness; prediction; sensory conflict theory; subjective vertical conflict theory.

FIGURE 1

Schematic block diagram of sensory conflict theory based on internal model hypothesis.

FIGURE 2

Schematic block diagram of sensory conflict theory capable of capturing the effect of human prediction of exogenous motion disturbance.

FIGURE 3

Block diagram of sensory conflict theory based on the internal model hypothesis.

FIGURE 4

(A–C) Motion profiles for three types of motion in the first 120 s.

FIGURE 5

Temporal histories of representative variables with the proposed model for every motion profile condition: (A) M_P, (B) M_dU, and (C) M_tU. The scalars ${\mathrm{\mu }}_t^p$ and $C_t^p$ denote the mean and variance of acceleration predicted by the proposed method, respectively. The error depicted on the second row was calculated as ${\mathrm{\mu }}_t^p-{\mathrm{\alpha }}_t$: the discrepancy between the predicted mean and the given acceleration of the exogenous disturbance. The gain k_pr was determined by Eq. (20) according to $C_t^p$. The predicted MSI by the proposed model is shown as the output of the model.

FIGURE 6

Predicted motion sickness incidence under different conditions.