GazePrompt: Enhancing Low Vision People's Reading Experience with Gaze-Aware Augmentations
February 20, 2024 Β· Declared Dead Β· π International Conference on Human Factors in Computing Systems
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Authors
Ru Wang, Zach Potter, Yun Ho, Daniel Killough, Linxiu Zeng, Sanbrita Mondal, Yuhang Zhao
arXiv ID
2402.12772
Category
cs.HC: Human-Computer Interaction
Citations
16
Venue
International Conference on Human Factors in Computing Systems
Last Checked
4 months ago
Abstract
Reading is a challenging task for low vision people. While conventional low vision aids (e.g., magnification) offer certain support, they cannot fully address the difficulties faced by low vision users, such as locating the next line and distinguishing similar words. To fill this gap, we present GazePrompt, a gaze-aware reading aid that provides timely and targeted visual and audio augmentations based on users' gaze behaviors. GazePrompt includes two key features: (1) a Line-Switching support that highlights the line a reader intends to read; and (2) a Difficult-Word support that magnifies or reads aloud a word that the reader hesitates with. Through a study with 13 low vision participants who performed well-controlled reading-aloud tasks with and without GazePrompt, we found that GazePrompt significantly reduced participants' line switching time, reduced word recognition errors, and improved their subjective reading experiences. A follow-up silent-reading study showed that GazePrompt can enhance users' concentration and perceived comprehension of the reading contents. We further derive design considerations for future gaze-based low vision aids.
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