Low vision individuals, while able to read with screen magnifiers, often experience slow and unpleasant reading. This research investigates how to effectively collect gaze data from low vision users using commercial eye trackers and explores their reading challenges through gaze behavior analysis. An improved calibration interface was used to collect gaze data from 20 low vision participants and 20 sighted controls during reading tasks. The study found that commercial eye trackers can collect comparable quality data from both groups with an accessible calibration interface. Unique gaze patterns in low vision readers were identified, providing design implications for gaze-based low vision technology.
Publisher
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23)
