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Zach Potter

dblp:371/1065 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Wearable and physiological sensing › eye tracking
gaze-based interaction
0.812024
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
0.812024
GazePrompt: Enhancing Low Vision People's Reading Experience with Gaze-Aware Augmentations · CHI 2024
Accessibility and assistive technology
reading assistance
0.812024
GazePrompt: Enhancing Low Vision People's Reading Experience with Gaze-Aware Augmentations · CHI 2024
Wearable and physiological sensing
eye tracking
0.212024
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
YearPublicationVenuePosition
2024 GazePrompt: Enhancing Low Vision People's Reading Experience with Gaze-Aware Augmentations
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.
Ru Wang 0002, Zach Potter, Yun Ho, Daniel Killough, Linxiu Zeng, Sanbrita Mondal, Yuhang Zhao 0001
CHI2