Related Experiment Video
Updated: Jun 22, 2025

07:36
Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
Published on: November 30, 2018
15.7K
(The limits of) eye-tracking with iPads
Aryaman Taore1,2, Michelle Tiang1,3, Steven C Dakin1,4,5
1School of Optometry & Vision Science, The University of Auckland, Auckland, New Zealand.
Journal of Vision
|July 2, 2024
Summary
Researchers compared iPad eye-tracking to dedicated hardware, finding the iPad less accurate for fixation but moderately correlated for saccades. Caution is advised due to accuracy limitations and tracking failures.
Area of Science:
- Ophthalmology
- Human-Computer Interaction
- Biomedical Engineering
Background:
- Clinical applications of eye-tracking are often hindered by the need for specialized, costly hardware.
- Mobile devices like iPads offer a potential low-cost alternative for eye-tracking research.
Purpose of the Study:
- To compare the accuracy and reliability of eye-tracking using an Apple iPad Pro (3rd generation) against a dedicated Tobii 4c infrared eye-tracker.
- To evaluate the performance of iPad-based eye-tracking for various oculomotor tasks.
Main Methods:
- 28 participants performed tasks while their gaze was tracked using both an iPad Pro's built-in sensors and a Tobii 4c eye-tracker.
- Gaze location, fixation accuracy, saccade characteristics (count, speed, amplitude), and smooth pursuit were analyzed.
Main Results:
- iPad eye-tracking showed lower accuracy and precision for fixation estimation compared to the Tobii 4c (3.2° ± 2.0° vs. 0.75° ± 0.43°).
- Correlations were found for fixation stability, saccade counts, saccade speed, and amplitude, particularly for larger saccades (>8°).
- Significant variations in smooth pursuit estimation and tracking failures (5-20%) were observed with the iPad.
Conclusions:
- While iPad eye-tracking shows moderate correlation for certain saccadic measures, its accuracy limitations necessitate careful data validation.
- Researchers should exercise caution when using iPads for eye-tracking, implementing robust artifact detection and outlier removal strategies.

