| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-11-06 |
| タイトル |
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タイトル |
Construction of a computational model of individual progression of motion sickness symptoms based on subjective vertical conflict theory |
| 言語 |
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言語 |
eng |
| キーワード |
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主題Scheme |
Other |
|
主題 |
Motion sickness |
| キーワード |
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主題Scheme |
Other |
|
主題 |
Computational model |
| キーワード |
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主題Scheme |
Other |
|
主題 |
Subjective vertical conflict theory |
| キーワード |
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主題Scheme |
Other |
|
主題 |
Individual symptom progression prediction |
| 資源タイプ |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Inoue, Shota
Dang, Van Trong
Liu, Hailong
和田, 隆広
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
Computational models predicting motion sickness have advanced, particularly those based on subjective vertical conflict (SVC) theory. While SVC-based models primarily predict motion sickness incidence (MSI), which is defined as the percentage of people who would vomit under a given motion, models predicting milder individual symptoms, which are crucial for daily applications, are still required. Recently, computational models predicting vestibular motion-sickness progression using the SVC theory have been developed by changing the output of a 6DOF-SVC model from MSI to the Misery Scale (MISC), a subjective measure of symptom progression. In practical applications, the ability to predict MISC for unseen motions is crucial. The present study conceived a method for predicting MISC beyond a certain point in the future by identifying parameters from data collected up to that point. Therefore, this study investigates the effect of the number of data points used for parameter identification on the future prediction accuracy. Observed MISC responses from participants exposed to linear lateral motion in darkness were used for model validation. The results indicated that prediction accuracy increased as more data points were included. On average, using more than 5–10 min of data significantly increased the accuracy compared to a model using averaged parameter sets across participants, although the tendency significantly differed based on an individual’s MISC history. A trial considering individual MISC histories, in which data points were defined when the observed MISC first reached certain levels, showed a general trend of improved accuracy when data up to MISC Level 3 was used. The findings of this study demonstrate that motion sickness symptom progression can be predicted with reduced error by incorporating individual symptom histories, thereby providing a foundation for the development of personalized motion sickness prediction models applicable to broader applications. |
| 書誌情報 |
en : Experimental Brain Research
巻 243,
号 5,
ページ数 18,
発行日 2025-04-05
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| 出版者 |
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出版者 |
Springer |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
1432-1106 |
| 出版者版DOI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.1007/s00221-025-07052-5 |
| 出版者版URI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
URI |
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関連識別子 |
https://link.springer.com/article/10.1007/s00221-025-07052-5 |
| 権利 |
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権利情報Resource |
https://creativecommons.org/licenses/by-nc-nd/4.0/ |
|
権利情報 |
© The Author(s) 2025. This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. |
| 著者版フラグ |
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出版タイプ |
NA |
| 助成情報 |
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助成機関名 |
Japan Society for the Promotion of Science (JSPS) |
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研究課題番号 |
21K18308 |
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研究課題番号URI |
https://kaken.nii.ac.jp/ja/grant/KAKENHI-PROJECT-21K18308/ |
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研究課題名 |
有人宇宙活動に向けた重力方向知覚に基づく宇宙酔いモデリングへの挑戦 |
| 助成情報 |
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助成機関名 |
Japan Society for the Promotion of Science (JSPS) |
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研究課題番号 |
24H00298 |
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研究課題番号URI |
https://kaken.nii.ac.jp/ja/grant/KAKENHI-PROJECT-24H00298/ |
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研究課題名 |
人間機械システムにおける適合性の計算論的理解とその向上手法の開発 |
