Eye-Hand Typing: Eye Gaze Assisted Finger Typing via Bayesian Processes in AR
Yunlei Ren, Yan Zhang, Zhitao Liu, Ning Xie
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.