Eliciting usable gestures for multi-display environments
Bibliographic record
Abstract
Multi-display environments (MDEs) have advanced rapidly in recent years, incorporating multi-touch tabletops, tablets, wall displays and even position tracking systems. Designers have proposed a variety of interesting gestures for use in an MDE, some of which involve a user moving their hands, arms, body or even a device itself. These gestures are often used as part of interactions to move data between the various components of an MDE, which is a longstanding research problem. But designers, not users, have created most of these gestures and concerns over implementation issues such as recognition may have influenced their design. We performed a user study to elicit these gestures directly from users, but found a low level of convergence among the gestures produced. This lack of agreement is important and we discuss its possible causes and the implication it has for designers. To assist designers, we present the most prevalent gestures and some of the underlying conceptual themes behind them. We also provide analysis of how certain factors such as distance and device type impact the choice of gestures and discuss how to apply them to real-world systems.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".