CountMarks: Multi-Finger Marking Menus for Mobile Interaction with Head-Mounted Displays
Bibliographic record
Abstract
Head-mounted displays (HMDs) are becoming thinner, lighter and wireless.Soon we may see these displays used in public in devices like smart glasses.In this thesis, we designed, implemented and evaluated a novel multi-touch marking menu technique for use with HMDs.CountMarks extends conventional marking menus (gesture-based radial menus) by using multi-finger input on a mobile phone screen.This supports selecting items from each of four menus (one for each finger) with a single swipe, reducing the need for deeper menu hierarchies.We discuss the design of two variations of CountMarks, exploring selection efficiency, public acceptability, and ergonomic comfort.We conduct two studies: the first compares CountMarks to a traditional marking menu and finds one variation of CountMarks makes faster selections and allows for better search accuracy with only a small reduction in selection accuracy.Our second study evaluates CountMarks while standing and walking and with interaction occurring on hand-held and leg-mounted devices.Our results show that CountMarks can be used in the hand while standing or walking, and we confirm the difficulties with leg interaction.We evaluate the types of errors made by participants to suggest improvements to CountMarks as a whole and for leg interaction in particular.Finally, we present an application demonstrating the implementation of CountMarks in an existing user interface and we suggest directions for future work.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".