Eye tracking on mobile devices is becoming increasingly important as they play a central role in our daily lives, from communication to entertainment and productivity. With the rise of mobile applications and user interface design, understanding user behavior through eye tracking offers valuable insights for enhancing user experience on mobile devices. Accurate eye-tracking technology on mobile platforms can reveal how users interact with their devices, which is essential for developing intuitive and effective applications.
To find the answer to this question, in RealEye we conducted a study in which we tested 4 calibration schemes with 13-, 22-, 27-, and 39-points. The system’s performance was tested across several screen regions and participant data quality. The goal was to find the optimal balance between calibration complexity and accuracy, offering valuable insights for researchers and practitioners conducting eye-tracking on Mobile Devices.
Accuracy and Precision are key metrics used to evaluate how well the eye-tracking system estimates participants' gaze points:
In case of Mobile Devices, where the accuracy-measuring task was based on fixations only and not on clicking the target, RealEye decided to calculate the distances between the center of each reference-target, and the center of the longest fixation on the target. This approach was chosen because longer fixations are typically more stable and indicative of focused attention, compensating for the lack of a precise referential point like a click.
Pixels were chosen as an accuracy measurement unit, instead of the more commonly used visual angle, due to the lack of control over participants' distance from the screen and their screen sizes, which are crucial when calculating visual angle. Therefore, using pixels allowed to maintain standard metrics that are independent of these variables, ensuring more consistent and reliable data analysis.
To learn more about RealEye's accuracy methodologies and findings on Computers, we invite you to read our Technology White Paper.
The table below summarizes the accuracy results for each calibration schema for Mobile Devices.
As the number of calibration points increases, the accuracy across screen areas improves. From the 13-point calibration, inaccuracies are prevalent, particularly in the peripheral areas, where the lower right region shows lower accuracy. Moving to the 22-point, 27-point, and 39-point calibrations, accuracy generally improves across both central and peripheral regions.
As the number of calibration points increases, accuracy improves across participant groups for all calibration schemes.
The Kolmogorov-Smirnov test for normality indicated that none of the datasets followed a normal distribution, necessitating the use of the Kruskal-Wallis H-test, which is a non-parametric method that compares distributions without sensitivity to imbalances. Following this, Dunn's test (with Bonferroni adjusted p-values) was used for post-hoc pairwise comparisons.
The post-hoc Dunn's test for minimum Good data quality revealed statistically significant differences in accuracy only between the 13-point and 39-point calibration schemas. No statistically significant differences were found between other calibration methods.
The statistically significant difference observed between the 13-point and 39-point calibrations is primarily due to the substantial increase in the number of reference points, particularly in the more challenging peripheral areas of the screen. Conversely, the lack of significant differences between the 13-point and 27-point calibrations, as well as between the 27-point and 39-point calibrations, can be explained by diminishing returns in accuracy improvements. Both the 27-point and 39-point methods demonstrate satisfactory performance across a wide range of tasks.
Although the 27-point calibration does not show a statistically significant advantage over the 22-point calibration, it is preferred due to its enhanced accuracy. The 27-point method provides more reference points than the 22-point calibration, which helps capture eye movements more effectively, especially in peripheral vision scenarios.
Given these findings, the 27-point calibration stands out as a balanced solution. It provides high accuracy while avoiding the added complexity associated with the 39-point method. Therefore, the 27-point calibration is an optimal choice for various applications in mobile eye tracking, effectively striking a balance between performance and usability.
Based on the results of this study, it is clear that the number of calibration points significantly affects the accuracy of eye-tracking on mobile devices. The 27-point calibration is therefore recommended as the optimal solution because it strikes a balance between accuracy and ease of calibration. It offers: