Wada Takahiro
College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan.
Front Syst Neurosci. 2021 Feb 9;15:634604. doi: 10.3389/fnsys.2021.634604. eCollection 2021.
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.
现有的用于估计晕动病的计算模型无法描述运动模式的可预测性会影响晕动病这一事实。因此，本研究提出了一种计算模型，以描述动力学可预测性或运动刺激模式对晕动病的影响。在所提出的模型中，一个子模型（其中使用递归高斯过程回归来表示人类在线学习和运动动力学未来预测的特征）与基于观察者理论的传统晕动病模型相结合。进行了一项模拟实验，在所提出的模型中预测了由900秒水平运动引起的晕动病。该运动由一个9米的重复来回运动模式和一个停顿组成。关于运动条件，运动的方向和停顿时间如下变化：(a) 可预测运动（M_P）：运动方向和停顿持续时间设定为8秒；(b) 方向不可预测的运动（M_dU）：停顿持续时间与（M_P）中相同，但运动方向随机确定；(c) 时间不可预测的运动（M_tU）：运动方向与（M_P）中相同，但停顿持续时间从4到12秒中随机选择。使用所提出的模型获得的结果表明，（M_P）的预测晕动病发病率低于（M_dU）和（M_tU），并且在M_dU和M_tU之间未发现显著差异。这种趋势与先前一项实验研究中观察到的晕动病模式一致，在该实验研究中，人类参与者所经历的运动条件与我们模拟中使用的条件相似。此外，当使用传统模型时，在不同条件下预测的晕动病发病率未发现显著差异。
