DESIGN AND MODELING

Accuracy of software-based eye tracking on consumer devices for color vision deficiency screening

Authors

  • Stanislav M. Santalov Plekhanov Russian University of Economics, 36 Stremyanny Lane, Moscow, 117997, Russia

How to cite

GOST Santalov S. M. Accuracy of software-based eye tracking on consumer devices for color vision deficiency screening // STROITEL'NYE I DOROZHNYE MASHINY. 2026. Vol. 70. No. 3. P. 202-210.
APA Santalov, S. M. (2026). Accuracy of software-based eye tracking on consumer devices for color vision deficiency screening. STROITEL'NYE I DOROZHNYE MASHINY, 70(3), 202-210.

Abstract

This narrative review analyzes the feasibility of using software-based eye tracking on consumer devices for objective screening of color vision deficiency (CVD). Published data on accuracy and sampling rates of software solutions across major platforms are systematized: iOS (TrueDepth / ARKit), Android (MediaPipe, ML Kit), and laptop webcams (WebGazer.js, deep learning models). Minimum eye-tracking requirements for recording optokinetic nystagmus (OKN) in response to chromatic stimuli are established. It is shown that modern consumer devices can provide sufficient accuracy and sampling rate for OKN recording and CVD screening under controlled conditions.

Keywords

color vision deficiency color blindness optokinetic nystagmus software-based eye tracking consumer devices ARKit MediaPipe WebGazer machine learning objective screening

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