Supporting Gaze-Based Interaction in a Visually Rich and Dynamic Environment with Machine Learning: An Online Study

Авторы: Shevtsova Yulia G., Yashin Artem S., Shishkin Sergei L., Vasilyev Anatoly N.
Сборник: Proceedings of the 2025 Symposium on Eye Tracking Research and Applications
Год издания: 2025
Издательство: ACM
Местоположение издательства: New York, N.Y., United States
Первая страница: 1
Последняя страница: 3
Номер статьи: 62
DOI: 10.1145/3715669.3726799
Аннотация: During gaze-based interaction, gaze provides both control and visual input. Although in simple tasks, like eye typing, these functions are separated, more complex scenarios can lead to misinterpretation of user intent. In our study, we explored if machine learning (ML) can aid in solving this problem. 15 participants played a gaze-controlled game, where they could freely select screen objects with a 500 ms dwell time. By applying ML to gaze features and contextual information, we achieved a threefold reduction in false positives. This study is the first to show how ML can enhance gaze-based interaction in visually demanding environments.
Добавил в систему: Васильев Анатолий Николаевич

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