GazeIntent: Adapting dwell-time selection in VR interaction with real-time intent modeling
<|reference_start|>GazeIntent: Adapting dwell-time selection in VR interaction with real-time intent modeling: 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<|reference_end|>
@article{narkar2024gazeintent:,
    title={GazeIntent: Adapting dwell-time selection in VR interaction with
  real-time intent modeling},
    author={Anish S. Narkar, Jan J. Michalak, Candace E. Peacock, and Brendan
  David-John},
    journal={arXiv preprint arXiv:2404.13829},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13829},
    primaryClass={cs.HC}
}