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一种用于预测线性和角向运动引起晕动病的视觉-前庭模型。

A Visual-Vestibular Model to Predict Motion Sickness for Linear and Angular Motion.

作者信息

Sousa Schulman Daniel, Jalgaonkar Nishant, Ojha Sneha, Rivero Valles Ana, Jones Monica L H, Awtar Shorya

机构信息

University of Michigan, Ann Arbor, MI, USA.

出版信息

Hum Factors. 2024 Aug;66(8):2120-2137. doi: 10.1177/00187208231200721. Epub 2023 Sep 12.

DOI:10.1177/00187208231200721
PMID:37699250
Abstract

OBJECTIVE

This study proposed a model to predict passenger motion sickness under the presence of a visual-vestibular conflict and assessed its performance with respect to previously recorded experimental data.

BACKGROUND

While several models have been shown useful to predict motion sickness under repetitive motion, improvements are still desired in terms of predicting motion sickness in realistic driving conditions. There remains a need for a model that considers angular and linear visual-vestibular motion inputs in three dimensions to improve prediction of passenger motion sickness.

METHOD

The model combined the subjective vertical conflict theory and human motion perception models. The proposed model integrates visual and vestibular sensed 6 DoF motion signals in a novel architecture.

RESULTS

Model prediction results were compared to motion sickness data obtained from studies conducted in motion simulators as well as on-road vehicle testing, yielding trends that are congruent with observed results in both cases.

CONCLUSION

The model demonstrated the ability to predict trends in motion sickness response for conditions in which a passenger performs a task on a handheld device versus facing forward looking ahead under realistic driving conditions. However, further analysis across a larger population is necessary to better assess the model's performance.

APPLICATION

The proposed model can be used as a tool to predict motion sickness under different levels of visual-vestibular conflict. This can be leveraged to design interventions capable of mitigating passenger motion sickness. Further, this model can provide insights that aid in the development of passenger experiences inside autonomous vehicles.

摘要

目的

本研究提出了一个模型,用于预测在视觉-前庭冲突情况下乘客的晕动病,并根据先前记录的实验数据评估其性能。

背景

虽然已有几个模型被证明在预测重复性运动下的晕动病方面有用,但在预测现实驾驶条件下的晕动病方面仍有改进的需求。仍然需要一个考虑三维角向和线性视觉-前庭运动输入的模型,以改善对乘客晕动病的预测。

方法

该模型结合了主观垂直冲突理论和人体运动感知模型。所提出的模型在一种新颖的架构中整合了视觉和前庭感知的6自由度运动信号。

结果

将模型预测结果与从运动模拟器研究以及道路车辆测试中获得的晕动病数据进行比较,在这两种情况下都得出了与观察结果一致的趋势。

结论

该模型展示了预测乘客在现实驾驶条件下在手持设备上执行任务与向前直视时晕动病反应趋势的能力。然而,需要对更多人群进行进一步分析,以更好地评估该模型的性能。

应用

所提出的模型可作为一种工具,用于预测不同程度视觉-前庭冲突下的晕动病。这可用于设计能够减轻乘客晕动病的干预措施。此外,该模型可为自动驾驶车辆内乘客体验的开发提供有帮助的见解。

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Hum Factors. 2024 Aug;66(8):2120-2137. doi: 10.1177/00187208231200721. Epub 2023 Sep 12.
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