BACKGROUND Head-mounted displays (HMDs) and virtual reality (VR) have been frequently used in recent years, and a user's experience and computation efficiency could be assessed by mounting eye-trackers. However, in addition to visually induced motion sickness (VIMS), eye fatigue has increasingly emerged during and after the viewing experience, highlighting the necessity of quantitatively assessment of the detrimental effects. As no measurement method for the eye fatigue caused by HMDs has been widely accepted, we detected parameters related to optometry test. We proposed a novel computational approach for estimation of eye fatigue by providing various verifiable models. RESULTS We implemented three classifications and two regressions to investigate different feature sets, which led to present two valid assessment models for eye fatigue by employing blinking features and eye movement features with the ground truth of indicators for optometry test. Three graded results and one continuous result were provided by each model, respectively, which caused the whole result to be repeatable and comparable. CONCLUSION We showed differences between VIMS and eye fatigue, and we also presented a new scheme to assess eye fatigue of HMDs users by analysis of parameters of the eye tracker.

中文翻译：

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使用眼动追踪技术评估由头戴式显示器引起的眼睛疲劳。

背景技术近年来，头戴式显示器（HMD）和虚拟现实（VR）已经被频繁使用，并且可以通过安装眼动仪来评估用户的体验和计算效率。但是，除了视觉诱发运动病（VIMS）之外，在观看体验期间和之后，眼睛疲劳也越来越多，这突出表明了对有害影响进行定量评估的必要性。由于尚未广泛接受由HMD引起的眼睛疲劳的测量方法，因此我们检测到与验光测试相关的参数。我们提出了一种新颖的计算方法，通过提供各种可验证的模型来估计眼睛疲劳。结果我们实施了三种分类和两种回归来研究不同的特征集，通过采用眨眼功能和眼动功能以及验光指标的真实性，得出了两种有效的眼疲劳评估模型。每个模型分别提供了三个分级结果和一个连续结果，这使得整个结果具有可重复性和可比性。结论我们展示了VIMS与眼睛疲劳之间的差异，并且我们还提出了一种通过分析眼动仪参数来评估HMD用户的眼睛疲劳的新方案。