GazeIntent: Adapting dwell-time selection in VR interaction with real-time intent modeling

被引：0
作者：
Narkar A.S. [1 ]
Michalak J.J. [1 ]
Peacock C.E. [2 ]
David-John B. [1 ]
机构：
[1] Virginia Tech, Blacksburg, 24060, VA
[2] Independent Researcher, Denver, 80005, CO
来源：
Proceedings of the ACM on Human-Computer Interaction | 2024年 / 8卷 / ETRA期
关键词：
algorithms; Eye movements and cognition; Gaze-controlled and hands-free interfaces; Gaze-input in augmented or mixed reality systems; Machine-learning methods; Novel systems; Predictive models; Task-specific evaluations;
D O I：
10.1145/3655600
中图分类号：
学科分类号：
摘要：
The use of ML models to predict a user’s cognitive state from behavioral data has been studied for various applications which includes predicting the intent to perform selections in VR. We developed a novel technique that uses gaze-based intent models to adapt dwell-time thresholds to aid gaze-only selection. A dataset of users performing selection in arithmetic tasks was used to develop intent prediction models (F1 = 0.94). We developed GazeIntent to adapt selection dwell times based on intent model outputs and conducted an end-user study with returning and new users performing additional tasks with varied selection frequencies. Personalized models for returning users effectively accounted for prior experience and were preferred by 63% of users. Our work provides the field with methods to adapt dwell-based selection to users, account for experience over time, and consider tasks that vary by selection frequency. © 2024 Copyright held by the owner/author(s).
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