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. 2025 Jun 6;15(12):1446.
doi: 10.3390/diagnostics15121446.

Validation of an Eye-Tracking Algorithm Based on Smartphone Videos: A Pilot Study

Wanzi Su  1 , Damon Hoad  2 , Leandro Pecchia  3 , Davide Piaggio  1
Affiliations

Validation of an Eye-Tracking Algorithm Based on Smartphone Videos: A Pilot Study

Wanzi Su et al. Diagnostics (Basel). .

Abstract

Introduction: This study aimed to develop and validate an efficient eye-tracking algorithm suitable for the analysis of images captured in the visible-light spectrum using a smartphone camera. Methods: The investigation primarily focused on comparing two algorithms, which were named CHT_TM and CHT_ACM, abbreviated from the core functions: Circular Hough Transform (CHT), Active Contour Models (ACMs), and Template Matching (TM). Results: CHT_TM significantly improved the running speed of the CHT_ACM algorithm, with not much difference in the resource consumption, and improved the accuracy on the x axis. CHT_TM achieved a reduction by 79% of the execution time. CHT_TM performed with an average mean percentage error of 0.34% and 0.95% in the x and y direction across the 19 manually validated videos, compared to 0.81% and 0.85% for CHT_ACM. Different conditions, like manually opening the eyelids with a finger versus without a finger, were also compared across four different tasks. Conclusions: This study shows that applying TM improves the original eye-tracking algorithm with CHT_ACM. The new algorithm has the potential to help the tracking of eye movement, which can facilitate the early screening and diagnosis of neurodegenerative diseases.

Keywords: biosignal processing; eye movement; eye-tracking; neurodegenerative condition.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
The initial set of the stimuli in the experiment protocol. (a) Vertical task; (b) horizontal task; (c) fixation task; and (d) circular task.
Figure 2
Figure 2
The experimental set-up.
Figure 3
Figure 3
The voting mechanism from the edge pixel in a circular pattern [61].
Figure 4
Figure 4
An example of matching the lower half of non-intact eye. (a) Before using masking. (b) After using masking.
Figure 5
Figure 5
The flowchart describes the steps of the algorithm.
Figure 6
Figure 6
The flowchart comparing the two different algorithms in this study.
Figure 7
Figure 7
The running speed of CHT_ACM and CHT_TM.
See this image and copyright information in PMC

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