用眼动追踪+大模型实现精准阅读进度跟踪，支持跳读和线性阅读。
See Where You Read with Eye Gaze Tracking and Large Language Model
- 基于眼动误差模型与大语言模型，实现跳读识别与阅读定位。
- 跳读跟踪准确率达84%，线性阅读跟踪稳定可靠。
- 适合需要高效阅读的用户，如研究人员、学生等。
阅读时换行错位会令人困扰。眼动追踪可通过高亮已读段落提供帮助，但其精度（2-3厘米）远低于文本行间距（3-5毫米），直接应用不现实。现有方法依赖线性阅读模式，无法应对跳读场景。本文提出一种支持线性和跳读的阅读跟踪与高亮系统。基于16名用户的实证研究，构建两种眼动误差模型以实现跳读检测与重定位。系统还利用大语言模型的上下文感知能力辅助阅读跟踪，并通过阅读领域特有的段落-眼动对齐机制，实现动态频繁校准。控制实验表明，系统在直线阅读中表现稳定，跳读跟踪准确率达84%。18名志愿者的真实场景测试显示，该系统能有效追踪并高亮已读段落，提升阅读效率，优化用户体验。
原文摘要 · Abstract (English)
Losing track of reading progress during line switching can be frustrating. Eye gaze tracking technology offers a potential solution by highlighting read paragraphs, aiding users in avoiding wrong line switches. However, the gap between gaze tracking accuracy (2-3 cm) and text line spacing (3-5 mm) makes direct application impractical. Existing methods leverage the linear reading pattern but fail during jump reading. This paper presents a reading tracking and highlighting system that supports both linear and jump reading. Based on experimental insights from the gaze nature study of 16 users, two gaze error models are designed to enable both jump reading detection and relocation. The system further leverages the large language model's contextual perception capability in aiding reading tracking. A reading tracking domain-specific line-gaze alignment opportunity is also exploited to enable dynamic and frequent calibration of the gaze results. Controlled experiments demonstrate reliable linear reading tracking, as well as 84% accuracy in tracking jump reading. Furthermore, real field tests with 18 volunteers demonstrated the system's effectiveness in tracking and highlighting read paragraphs, improving reading efficiency, and enhancing user experience.
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