Digital human models of motion sickness have been actively developed, among which models based on subjective vertical conflict (SVC) theory are the most actively studied. These models facilitate the prediction of motion sickness in various scenarios such as riding in a car. Most SVC theory models predict the motion sickness incidence (MSI), which is defined as the percentage of people who would vomit with the given specific motion stimulus. However, no model has been developed to describe milder forms of discomfort or specific symptoms of motion sickness, even though predicting milder symptoms is important for applications in automobiles and daily use vehicles. Therefore, the purpose of this study was to build a computational model of symptom progression of vestibular motion sickness based on SVC theory. We focused on a model of vestibular motion sickness with six degrees-of-freedom (6DoF) head motions. The model was developed by updating the output part of the state-of-the-art SVC model, termed the 6DoF-SVC (IN1) model, from MSI to the MIsery SCale (MISC), which is a subjective rating scale for symptom progression. We conducted an experiment to measure the progression of motion sickness during a straight fore-aft motion. It was demonstrated that our proposed method, with the parameters of the output parts optimized by the experimental results, fits well with the observed MISC.

翻译：运动眩晕症的数字人体模型已被积极开发，其中基于主观垂直冲突（SVC）理论的模型是研究最为活跃的。这些模型有助于预测各种场景下的运动眩晕症，例如乘车。大多数SVC理论模型预测的是运动眩晕发生率（MSI），其定义为在给定特定运动刺激下会出现呕吐症状的人群百分比。然而，尽管预测较轻症状对于汽车及日常使用车辆的应用至关重要，目前尚未开发出能够描述运动眩晕症较轻微不适或具体症状的模型。因此，本研究旨在构建一个基于SVC理论的前庭性运动眩晕症症状进展的计算模型。我们专注于一个具有六自由度（6DoF）头部运动的前庭性运动眩晕症模型。该模型通过将最先进的SVC模型（称为6DoF-SVC（IN1）模型）的输出部分从MSI更新为痛苦量表（MISC）——一种用于评估症状进展的主观评分量表——而开发。我们进行了一项实验，以测量直线前后运动期间运动眩晕症的进展过程。结果表明，我们提出的方法（其输出部分的参数通过实验结果进行了优化）与观测到的MISC数据拟合良好。