GazeIntent: Adapting dwell-time selection in VR interaction with real-time intent modeling
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866914764255395840 |
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| author | Narkar, Anish S. Michalak, Jan J. Peacock, Candace E. David-John, Brendan |
| author_facet | Narkar, Anish S. Michalak, Jan J. Peacock, Candace E. David-John, Brendan |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_13829 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GazeIntent: Adapting dwell-time selection in VR interaction with real-time intent modeling Narkar, Anish S. Michalak, Jan J. Peacock, Candace E. David-John, Brendan Human-Computer Interaction 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 |
| title | GazeIntent: Adapting dwell-time selection in VR interaction with real-time intent modeling |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2404.13829 |