GraspR: A Computational Model of Spatial User Preferences for Adaptive Grasp UI Design

DOI

Grasp User Interfaces (grasp UIs) enable dual-tasking in XR by allowing interaction with digital content while holding physical objects. However, designing grasp UIs presents a fundamental challenge: existing approaches either capture user preferences through labor-intensive elicitation studies that don't scale, or rely on biomechanical models that ignore 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 dataset and code to support future research in adaptive grasp UIs.