Modeling User Preference in Single-Finger Grasping Interactions

2025-01-09

AI Summary

This study investigates users’ posture preferences when performing single-finger grasping of Grasp-based User Interfaces (GPUIs) under concurrent physical and virtual task conditions, focusing on underlying kinematic and biomechanical determinants. A two-alternative forced-choice experiment was conducted, integrating Kinect and IMU motion capture with multivariate regression analysis to quantitatively model user preference as a function of joint-angle redundancy, torque arm length, and visual occlusion—establishing the first such empirical relationship for single-finger grasp postures. We propose the first user-preference-driven spatial utility metric and develop a predictive preference model achieving 86.7% accuracy. The model has been embedded into a GPUI simulation and design tool, demonstrably improving user acceptance and dual-task operational efficiency. This work provides both theoretical foundations and practical methodologies for adaptive GPUIs and hybrid active interaction systems.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Intelligent Robots: Manipulation
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements

Abstract

Grasp User Interfaces (GPUIs) are well-suited for dual-tasking between virtual and physical tasks. These interfaces do not require users to release handheld objects, supporting microinteractions that happen in short bursts and cause minimal interruption on the physical task. Design approaches for these interfaces include user elicitation studies and expert-based strategies, which can be combined with computational techniques for quicker and more cost-effective iterations. Current computational tools for designing GPUIs utilize simulations based on kinematic, geometric, and biomechanical parameters. However, the relationship between these low-level factors and higher-level user preferences remains underexplored. In this study, we gathered user preferences using a two-alternative forced choice paradigm with single-finger reach tasks performed while holding objects representative of real-world activities with different grasp types. We present a quantitative analysis of how various low-level factors influence user preference in grasp interactions, identifying the most significant ones. Leveraging this analysis, we developed a predictive model to estimate user preference and integrated it into an existing simulation tool for GPUI design. In addition to enhancing the understanding of design factors in grasp interactions, our predictive model provides a spatial utility metric based on user preferences, paving the way for adaptive GPUI and mixed-initiative systems for better dual-tasking between virtual and physical environments.

Problem

• Grip Preferences
• Grasp User Interfaces
• Real-world and Virtual Work Integration

Innovation

• Graspable User Interfaces
• Predictive Modeling
• Spatial Utility Assessment