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.