Demo Data from SIGIR 2026 / reviewers in the wild / expert
Zach Potter
dblp:371/1065
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0009-9692-2272ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Accessibility and assistive technology · 61% Wearable and physiological sensing · 39% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Wearable and physiological sensing › eye tracking
gaze-based interaction |
0.8 | 1 | 2024 | GazePrompt: Enhancing Low Vision People's Reading Experience with Gaze-Aware Augmentations · CHI 2024 |
Accessibility and assistive technology › assistive technology for visual impairment
low-vision reading aids |
0.8 | 1 | 2024 | GazePrompt: Enhancing Low Vision People's Reading Experience with Gaze-Aware Augmentations · CHI 2024 |
Accessibility and assistive technology
reading assistance |
0.8 | 1 | 2024 | GazePrompt: Enhancing Low Vision People's Reading Experience with Gaze-Aware Augmentations · CHI 2024 |
Wearable and physiological sensing
eye tracking |
0.2 | 1 | 2024 | GazePrompt: Enhancing Low Vision People's Reading Experience with Gaze-Aware Augmentations · CHI 2024 |
Methods — techniques the papers use, named apart from their topics
user study · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | GazePrompt: Enhancing Low Vision People's Reading Experience with Gaze-Aware AugmentationsabstractReading 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. Ru Wang 0002, Zach Potter, Yun Ho, Daniel Killough, Linxiu Zeng, Sanbrita Mondal, Yuhang Zhao 0001 |
CHI | 2 |