[观影时眼动特征分析及其应用]。

Q3 Medicine
Akihiro Sugiura, Rentaro Ono, Yoshiki Itazu, Hibiki Sakakura, Hiroki Takada
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引用次数: 0

摘要

在这篇文章中,我们介绍了以下内容:视动病(VIMS)的研究背景,我们的研究目的,以及我们小组最近对观看电影时眼动的测量和分析以及眼动与VIMS的关系进行的三项研究的描述。首先,本研究关注眼动与晕动病易感性之间的关系。我们研究了晕动病易感性与外周视动性眼球震颤(OKN)发生频率的关系。结果显示,在强烈支持OKN发生的条件下,易感参与者比不易感参与者表现出更低的OKN频率。其次,本研究重点研究了视觉信息与姿势变化(如视觉诱发姿势反应)之间的关系。在这项研究中,研究人员测量了观看电影时的眼球运动和重心。此外,由于电影内容的类型或观看方式不同,我们评估了传递函数(视觉作为输入,平衡函数作为输出)的传递增益的差异。外围观看的三维电影的增益大于中心观看的二维电影。第三,本研究的重点是眼动和深度学习技术的应用。在这项研究中,我们使用带有监督学习的卷积深度神经网络将眼球运动分为外围或中心。然后进行交叉验证,检验分类准确率。使用>1-s的眼动数据产生了>90%的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
[Analysis of Characteristics of Eye Movement While Viewing Movies and Its Application].

In this article, we present the following: a background of visually induced motion sickness (VIMS), the goal of our study, and descriptions of three recent studies conducted by our group on the measurement and analysis of eye movement while viewing movies and the relationship of eye movement with VIMS. First, this study focuses on the relationship between eye movement and motion sickness susceptibility. We investigated the relationship between the motion sickness susceptibility and the frequency of optokinetic nystagmus (OKN) with peripheral viewing. It was revealed that susceptible participants showed a lower OKN frequency under conditions that strongly support the occurrence of OKN than insusceptible participants. Second, this study focuses on the relationship between visual information and postural variation such as visually evoked postural responses (VEPRs). In this study, both eye movement and the center of gravity while viewing a movie were measured. Additionally, we evaluated the difference in the transfer gain of the transfer function (vision as input and equilibrium function as output) due to the type of movie content or way of viewing. The gain for the three-dimensional movie with peripheral viewing exceeded that for the two-dimensional movie with central viewing. Third, this study focuses on eye movement and the application of deep-learning technology. In this study, we classified the eye movement as peripheral or central using a convolutional deep neural network with supervised learning. Then, cross validation was performed to test the classification accuracy. The use of >1-s eye movement data yielded an accuracy of >90%.

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来源期刊
Japanese Journal of Hygiene
Japanese Journal of Hygiene Medicine-Medicine (all)
CiteScore
0.90
自引率
0.00%
发文量
7
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