GraV: Grasp Volume Data for the Design of One-Handed XR Interfaces

TL;DR

This paper introduces GraV, a simulation tool and dataset for designing one-handed XR interfaces based on grasp reachability and displacement data, addressing a gap in XR interface design resources.

Contribution

We present GraV, a novel simulation tool and dataset that provide reachability and displacement data for designing grasp-based XR interfaces, filling a key gap in current XR design resources.

Findings

Generated a dataset based on grasp taxonomy and household objects.

Identified critical design factors for grasp-proximate XR interfaces.

Workshop insights highlight the importance of reachability and motion cost data.

Abstract

Everyday objects, like remote controls or electric toothbrushes, are crafted with hand-accessible interfaces. Expanding on this design principle, extended reality (XR) interfaces for physical tasks could facilitate interaction without necessitating the release of grasped tools, ensuring seamless workflow integration. While established data, such as hand anthropometric measurements, guide the design of handheld objects, XR currently lacks comparable data, regarding reachability, for single-hand interfaces while grasping objects. To address this, we identify critical design factors and a design space representing grasp-proximate interfaces and introduce a simulation tool for generating reachability and displacement cost data for designing these interfaces. Additionally, using the simulation tool, we generate a dataset based on grasp taxonomy and common household objects. Finally, we share insights from a design workshop that emphasizes the significance of reachability and motion cost data, empowering XR creators to develop bespoke interfaces tailored specifically to grasping hands.