IE СТРОИТЕЛЬНЫЕ И ДОРОЖНЫЕ МАШИНЫ STROITEL'NYE I DOROZHNYE MASHINY 0039-2391 ИП Подколзин М.М. 284 ПРОЕКТИРОВАНИЕ И МОДЕЛИРОВАНИЕ Точность программного айтрекинга на потребительских устройствах в задачах скрининга нарушений цветового зрения Accuracy of software-based eye tracking on consumer devices for color vision deficiency screening Санталов Станислав Максимович Santalov Stanislav M. santalov.stas@gmail.com Российский экономический университет им. Г.В. Плеханова, 117997, г. Москва, Стремянный пер., д. 36 Plekhanov Russian University of Economics, 36 Stremyanny Lane, Moscow, 117997, Russia 30 03 2026 70 3 202 210 © 2026 СТРОИТЕЛЬНЫЕ И ДОРОЖНЫЕ МАШИНЫ. Все права защищены. © 2026 STROITEL'NYE I DOROZHNYE MASHINY. All rights reserved. 2026 СТРОИТЕЛЬНЫЕ И ДОРОЖНЫЕ МАШИНЫ STROITEL'NYE I DOROZHNYE MASHINY Метаданные настоящей записи распространяются на условиях Creative Commons CC0 1.0 (передача в общественное достояние). The metadata of this record are distributed under the Creative Commons CC0 1.0 Universal Public Domain Dedication.

Настоящий повествовательный обзор посвящен анализу возможности использования программного айтрекинга на потребительских устройствах для объективного скрининга нарушений цветового зрения (НЦЗ). Систематизированы данные о точности и частоте дискретизации программных решений на платформах iOS (TrueDepth / ARKit), Android (MediaPipe, ML Kit), веб-камеры ноутбуков (WebGazer.js, модели глубокого обучения). Определены минимальные требования к айтрекингу для регистрации оптокинетического нистагма (ОКН) в ответ на хроматические стимулы. Показано, что современные потребительские устройства способны обеспечить достаточную точность для скрининга НЦЗ в контролируемых условиях.

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.

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