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

TL;DR

This work identifies critical design factors and a design space representing grasp-proximate interfaces and introduces a simulation tool for generating reachability and displacement cost data for designing these interfaces, empowering XR creators to develop bespoke interfaces tailored specifically to grasping hands.

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.

Figures (11)

• Figure 1: Hand Grasp and finger Interaction Parts. In (A), the brown region represents the part of the hand that is used for grip (i.e., the palm), the green represents the fingers that can move freely (index and thumb), and the blue dots represent the fingertips (both pads and nails) as the finger interaction parts. (b1) and (b2) represent the same grip but are adjusted to prioritize thumb and index finger mobility, respectively. (b3) shows both fingers are available to move while holding the screwdriver.
• Figure 2: Hand Motion Tracking and Cost Evaluation. (a1) shows a 2D representation of the finger motion range for the index finger, while (a2) shows a 2D representation of the accessible interaction volume for the index fingertip. (b1) and (b2) represent a lower cost and a higher cost motion for the index finger, respectively.
• Figure 3: Object and motion boundaries. (A) shows a free-hand range of motion, and (B) shows the object boundary that is formed when an object's volume intersects with the hand's free-motion volume. (C) represents the motion boundary for both the index finger and thumb while holding a screwdriver with no external obstruction. (D) Shows the opportunistic surface haptic available for the index finger.
• Figure 4: Design space for GPUIs. (A) represents a change in grasp type keeping the user's hand and object constant. (B) represents a change in an object while keeping the other two axes constant. (C) represents the same object and grasp type for the same user but in condition H2 the thumb has an injury that reduces its range of motion.
• Figure 5: Procedural hand generated in the simulation process from anthropometric parameters. The forward direction points in the direction of the index. The right direction points from the index MP to the little finger MP. Positive rotations around the X-axis are counterclockwise.