Abstract

Predicting the time course of motion sickness symptoms enables the evaluation of provocative stimuli and the development of countermeasures for reducing symptom severity. In pursuit of this goal, we present an Observer-driven model of motion sickness for passive motions in the dark. Constructed in two stages, this model predicts motion sickness symptoms by bridging sensory conflict (i.e., differences between actual and expected sensory signals) arising from the Observer model of spatial orientation perception (stage 1) to Oman’s model of motion sickness symptom dynamics (stage 2; presented in 1982 and 1990) through a proposed “Normalized Innovation Squared” statistic. The model outputs the expected temporal development of human motion sickness symptom magnitudes (mapped to the Misery Scale) at a population level, due to arbitrary, 6-degree-of-freedom, self-motion stimuli. We trained model parameters using individual subject responses collected during fore-aft translations and off-vertical axis of rotation motions. Improving on prior efforts, we only used datasets with experimental conditions congruent with the perceptual stage (i.e., adequately provided passive motions without visual cues) to inform the model. We assessed model performance by predicting an unseen validation dataset, producing a Q² value of 0.91. Demonstrating this model’s broad applicability, we formulate predictions for a host of stimuli, including translations, earth-vertical rotations, and altered gravity, and we provide our implementation for other users. Finally, to guide future research efforts, we suggest how to rigorously advance this model (e.g., incorporating visual cues, active motion, responses to motion of different frequency, etc.).

中文翻译：

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黑暗中被动自我运动过程中晕动病动力学的计算模型

摘要

预测晕动病症状的时间进程可以评估刺激刺激并制定减轻症状严重程度的对策。为了实现这一目标，我们提出了一种观察者驱动的黑暗中被动运动的晕动病模型。该模型分两个阶段构建，通过桥接空间方向感知观察者模型（第一阶段）和阿曼晕动病症状动力学模型（第二阶段）产生的感觉冲突（即实际感觉信号与预期感觉信号之间的差异）来预测晕动病症状；于 1982 年和 1990 年提出）通过拟议的“标准化创新平方”统计数据。该模型输出由于任意的 6 自由度自我运动刺激，人类晕动病症状强度（映射到痛苦量表）在人群水平上的预期时间发展。我们使用在前后平移和偏离垂直轴的旋转运动期间收集的个体响应来训练模型参数。改进之前的工作，我们仅使用具有与感知阶段一致的实验条件的数据集（即，充分提供没有视觉提示的被动运动）来为模型提供信息。我们通过预测未见过的验证数据集来评估模型性能，产生的Q ²值为 0.91。为了证明该模型的广泛适用性，我们对一系列刺激进行了预测，包括平移、地球垂直旋转和重力变化，并为其他用户提供了实现。最后，为了指导未来的研究工作，我们建议如何严格推进该模型（例如，结合视觉线索、主动运动、对不同频率运动的响应等）。