基于投影的立体显示中眼球注视精度与注视次数、眼球运动时间和视差的关系

Y. Prasetyo, R. Widyaningrum, C. Lin
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引用次数: 14

摘要

眼球注视精度是评价眼动仪性能最常用的参数之一。本研究采用逐步多元回归方法预测注视精度(AC)的显著预测因子。选取7名男性和3名女性,在3种不同视差水平和6种不同难度指数(ID)下完成投影立体显示中的多向点触任务。采用Tobii X2眼动仪测量选定的4项眼动参数:注视次数(NF)、注视持续时间(FD)、首次注视时间(TFF)和眼动时间(EMT)。结果表明,NF是AC的最佳预测因子,其次是EMT和视差。R2值为0.247,表明24.7%的数据变异性被模型解释。从业者总结:多元回归的结果可以为评估眼动仪提供有价值的理论基础。研究结果对人为因素工程师和虚拟现实开发人员非常有益,特别是在预测眼睛注视精度方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Eye Gaze Accuracy in the Projection-based Stereoscopic Display as a Function of Number of Fixation, Eye Movement Time, and Parallax
Eye gaze accuracy is one of the most commonly used parameter to evaluate the eye tracker performance. The current study applied stepwise multiple regression to predict the significant predictors for eye gaze accuracy (AC). 7 male and 3 female were recruited to perform multi-directional tapping task in the projection-based stereoscopic display under 3 different levels of parallax and 6 different levels of index of difficulty (ID). Tobii X2 eye tracker was used to measure the selected four eye movement parameters which consist number of fixation (NF), fixation duration (FD), time to first fixation (TFF), and eye movement time (EMT). The results indicated that NF was found to be the best predictor for AC followed by EMT and parallax. The R2 value of 0.247 indicating that the 24.7% of the variability of the data was explained by the model. Practitioner Summary: The result of multiple regression can be a valuable theoretical foundation for evaluating an eye tracker. The results could be very beneficial for human factors engineers and virtual reality developers especially for predicting eye gaze accuracy.
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