An Elicitation Study on Gesture Attitudes and Preferences Towards an Interactive Hand-Gesture Vocabulary
Bibliographic record
Abstract
With the introduction of new depth sensing technologies, interactive hand-gesture devices are rapidly emerging. However, the hand-gestures used in these devices do not follow a common vocabulary, making certain control command device-specific. In this paper we present an initial effort to create a standardized interactive hand-gesture vocabulary for the next generation of television applications. We conduct a user-elicitation study using a survey in order to define a common vocabulary for specific control commands, such as Volume up/down, Menu open/close, etc. This survey is entirely user-oriented and thus it has two phases. In the first phase, we ask open questions about specific commands. In the second phase, we use the answers suggested from the first phase to create a multiple choice questionnaire. Based on the results from the survey, we study the gesture attitudes and preferences between gender groups, and between age groups with a quantitative and qualitative statistical analysis. Finally, the hand-gesture vocabulary is derived after applying an agreement analysis on the user-elicited gestures. The proposed methodology for gesture set design is comparable with existing methodologies and yields higher agreement levels than relevant user-elicited studies in the field.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".