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眼手打字:通过 AR 中的贝叶斯过程实现的眼动辅助手指打字。

Eye-Hand Typing: Eye Gaze Assisted Finger Typing via Bayesian Processes in AR.

出版信息

IEEE Trans Vis Comput Graph. 2024 May;30(5):2496-2506. doi: 10.1109/TVCG.2024.3372106. Epub 2024 Apr 19.

Abstract

Nowadays, AR HMDs are widely used in scenarios such as intelligent manufacturing and digital factories. In a factory environment, fast and accurate text input is crucial for operators' efficiency and task completion quality. However, the traditional AR keyboard may not meet this requirement, and the noisy environment is unsuitable for voice input. In this article, we introduce Eye-Hand Typing, an intelligent AR keyboard. We leverage the speed advantage of eye gaze and use a Bayesian process based on the information of gaze points to infer users' text input intentions. We improve the underlying keyboard algorithm without changing user input habits, thereby improving factory users' text input speed and accuracy. In real-time applications, when the user's gaze point is on the keyboard, the Bayesian process can predict the most likely characters, vocabulary, or commands that the user will input based on the position and duration of the gaze point and input history. The system can enlarge and highlight recommended text input options based on the predicted results, thereby improving user input efficiency. A user study showed that compared with the current HoloLens 2 system keyboard, Eye-Hand Typing could reduce input error rates by 28.31 % and improve text input speed by 14.5%. It also outperformed a gaze-only technique, being 43.05% more accurate and 39.55% faster. And it was no significant compromise in eye fatigue. Users also showed positive preferences.

摘要

如今,AR HMD 广泛应用于智能制造和数字工厂等场景。在工厂环境中,操作人员的效率和任务完成质量对快速、准确的文本输入要求很高。但是,传统的 AR 键盘可能无法满足这一要求,嘈杂的环境也不适合语音输入。在本文中,我们介绍了 Eye-Hand Typing,这是一种智能 AR 键盘。我们利用眼动注视的速度优势,使用基于注视点信息的贝叶斯过程来推断用户的文本输入意图。我们在不改变用户输入习惯的情况下改进底层键盘算法,从而提高工厂用户的文本输入速度和准确性。在实时应用中,当用户的注视点位于键盘上时,贝叶斯过程可以根据注视点的位置和持续时间以及输入历史,预测用户最有可能输入的字符、词汇或命令。系统可以根据预测结果放大并突出显示推荐的文本输入选项,从而提高用户输入效率。用户研究表明,与当前的 HoloLens 2 系统键盘相比,Eye-Hand Typing 可以将输入错误率降低 28.31%,将文本输入速度提高 14.5%。它也优于仅使用注视的技术,准确率提高了 43.05%,速度提高了 39.55%。并且在眼疲劳方面没有显著的妥协。用户也表现出积极的偏好。

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