GraspR: A Computational Model of Spatial User Preferences for Adaptive Grasp UI Design
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908528294232064 |
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| author | Caetano, Arthur Luo, Yunhao Sharma, Adwait Sra, Misha |
| author_facet | Caetano, Arthur Luo, Yunhao Sharma, Adwait Sra, Misha |
| contents | Grasp User Interfaces (grasp UIs) enable dual-tasking in XR by allowing interaction with digital content while holding physical objects. However, current grasp UI design practices face a fundamental challenge: existing approaches either capture user preferences through labor-intensive elicitation studies that are difficult to scale or rely on biomechanical models that overlook subjective factors. We introduce GraspR, the first computational model that predicts user preferences for single-finger microgestures in grasp UIs. Our data-driven approach combines the scalability of computational methods with human preference modeling, trained on 1,520 preferences collected via a two-alternative forced choice paradigm across eight participants and four frequently used grasp variations. We demonstrate GraspR's effectiveness through a working prototype that dynamically adjusts interface layouts across four everyday tasks. We release both the dataset and code to support future research in adaptive grasp UIs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_05434 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GraspR: A Computational Model of Spatial User Preferences for Adaptive Grasp UI Design Caetano, Arthur Luo, Yunhao Sharma, Adwait Sra, Misha Human-Computer Interaction H.5.2 Grasp User Interfaces (grasp UIs) enable dual-tasking in XR by allowing interaction with digital content while holding physical objects. However, current grasp UI design practices face a fundamental challenge: existing approaches either capture user preferences through labor-intensive elicitation studies that are difficult to scale or rely on biomechanical models that overlook subjective factors. We introduce GraspR, the first computational model that predicts user preferences for single-finger microgestures in grasp UIs. Our data-driven approach combines the scalability of computational methods with human preference modeling, trained on 1,520 preferences collected via a two-alternative forced choice paradigm across eight participants and four frequently used grasp variations. We demonstrate GraspR's effectiveness through a working prototype that dynamically adjusts interface layouts across four everyday tasks. We release both the dataset and code to support future research in adaptive grasp UIs. |
| title | GraspR: A Computational Model of Spatial User Preferences for Adaptive Grasp UI Design |
| topic | Human-Computer Interaction H.5.2 |
| url | https://arxiv.org/abs/2501.05434 |