PalmGazer: Unimanual Eye-hand Menus in Augmented Reality
Bibliographic record
Abstract
How can we design the user interfaces for augmented reality (AR) so that we can interact as simple, flexible and expressive as we can with smartphones in one hand? To explore this question, we propose PalmGazer as an interaction concept integrating eye-hand interaction to establish a singlehandedly operable menu system. In particular, PalmGazer is designed to support quick and spontaneous digital commands– such as to play a music track, check notifications or browse visual media – through our devised three-way interaction model: hand opening to summon the menu UI, eye-hand input for selection of items, and dragging gesture for navigation. A key aspect is that it remains always-accessible and movable to the user, as the menu supports meaningful hand and head based reference frames. We demonstrate the concept in practice through a prototypical mobile UI with application probes, and describe technique designs specifically-tailored to the application UI. A qualitative evaluation highlights the system’s interaction benefits and drawbacks, e.g., that common 2D scroll and selection tasks are simple to operate, but higher degrees of freedom may be reserved for two hands. Our work contributes interaction techniques and design insights to expand AR’s uni-manual capabilities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".