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

TL;DR

GraspR is a novel computational model that predicts user preferences for microgestures in grasp UIs, enabling adaptive interface design in XR with scalable, data-driven methods.

Contribution

It introduces the first data-driven model for predicting user preferences in grasp UIs, combining scalability with subjective preference modeling.

Findings

Successfully trained on 1,520 preferences from 8 users

Demonstrated effective dynamic interface adjustments in prototypes

Released dataset and code for future research

Abstract

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.