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

## TL;DR

This pilot study developed and validated a faster and more accurate eye-tracking algorithm using smartphone videos, which could aid in early detection of neurodegenerative diseases.

## Contribution

A novel eye-tracking algorithm (CHT_TM) that improves speed and accuracy compared to CHT_ACM.

## Key findings

- CHT_TM reduced execution time by 79% compared to CHT_ACM with similar resource consumption.
- CHT_TM achieved lower mean percentage errors (0.34% and 0.95%) in x and y directions compared to CHT_ACM.
- The algorithm was tested under different eyelid conditions across four tasks.

## 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.