用眼球轨迹检测VR视觉疲劳，准确率最高达94%。
Deep Learning-Based Visual Fatigue Detection Using Eye Gaze Patterns in VR
- 基于深度学习分析用户在VR中的连续眼球运动轨迹
- 在视频与阅读任务中准确率达94%，显著优于传统方法
- 无需侵入式设备，适合实时应用与长期监测
长时间使用虚拟现实（VR）系统会导致视觉疲劳，影响用户体验、表现与安全，尤其在高风险或长时间场景中。现有疲劳检测方法依赖主观问卷或侵入式生理信号（如脑电图、心率、眨眼频率），限制了其可扩展性与实时性。本文提出一种基于深度学习的视觉疲劳检测方法，利用VR中连续的眼球轨迹数据。研究采用包含407名参与者五项沉浸式任务的GazeBaseVR数据集，提取单眼眼动角度，评估六种深度分类器。结果表明，EKYT模型在视频观看和文本阅读等高注意力任务中准确率高达94%。进一步分析显示，疲劳状态下眼球运动方差与主观疲劳量表存在显著差异。这些发现证实眼动动态是沉浸式VR中可靠且非侵入式的持续疲劳检测方式，为自适应人机交互提供了实用价值。
原文摘要 · Abstract (English)
Prolonged exposure to virtual reality (VR) systems leads to visual fatigue, impairs user comfort, performance, and safety, particularly in high-stakes or long-duration applications. Existing fatigue detection approaches rely on subjective questionnaires or intrusive physiological signals, such as EEG, heart rate, or eye-blink count, which limit their scalability and real-time applicability. This paper introduces a deep learning-based study for detecting visual fatigue using continuous eye-gaze trajectories recorded in VR. We use the GazeBaseVR dataset comprising binocular eye-tracking data from 407 participants across five immersive tasks, extract cyclopean eye-gaze angles, and evaluate six deep classifiers. Our results demonstrate that EKYT achieves up to 94% accuracy, particularly in tasks demanding high visual attention, such as video viewing and text reading. We further analyze gaze variance and subjective fatigue measures, indicating significant behavioral differences between fatigued and non-fatigued conditions. These findings establish eye-gaze dynamics as a reliable and nonintrusive modality for continuous fatigue detection in immersive VR, offering practical implications for adaptive human-computer interactions.
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