Problem
Research questions and friction points this paper is trying to address.
on-palm interaction
touch modeling
smart glasses input
bare palm sensing
mixed reality
PalmSpace: Towards a Versatile On-Palm Interaction Space through Unified Touch Modeling
🤖 AI Summary
This study addresses the input limitations of smart glasses caused by the absence of a coordinate system and sensing capability on bare palms. We propose a calibration-free absolute coordinate interaction system based on wrist-worn infrared sensing. The system employs a unified representation framework that fuses contact, pattern, and positional information, combined with a shared real-time representation model and multi-task joint learning algorithm to transform the bare palm into a versatile interaction surface without user-specific calibration. Experimental results demonstrate that the system achieves a positioning error of only 6.7 mm, a contact detection accuracy of 98.9%, and a state recognition F1-score of 96.7%. These findings confirm its effectiveness in supporting precise selection and diverse gesture-based manipulations for smart glasses.
📝 Abstract
As smart glasses and lightweight MR devices become increasingly practical, input remains a key challenge. The bare palm is an always-available, tactile, and proprioceptively accessible surface, but it has neither an explicit coordinate system nor embedded touch sensing. Prior on-palm systems typically expose isolated touch events, discrete regions, continuous trajectories, or task-specific gestures, limiting the palm's ability to support precise selection and gesture manipulation through a common input representation. We present PalmSpace, a wrist-worn infrared system that exposes mode-aware, body-referenced absolute input on the bare palm without per-user sensing calibration. At the interaction level, PalmSpace jointly represents contact occurrence, interaction mode, and palm-referenced absolute location; at the model level, it learns these coupled outputs through a shared real-time representation. In leave-one-participant-out evaluation with 17 participants, PalmSpace achieved 6.7 mm mean localization error, 98.9% contact detection accuracy, and 96.7% F1 for four-class interaction-state recognition. User studies further demonstrated absolute pointing and dragging, eyes-free digit input, and representative multi-finger controls including scrolling and pinch-based map manipulation. These results show that a morphologically variable bare palm can function as a transferable, mode-aware interaction surface.
Innovation
Methods, ideas, or system contributions that make the work stand out.
On-palm interaction
Unified touch modeling
Wrist-worn infrared sensing
Absolute localization
Cross-user generalization
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