Leveraging Smartwatch and Earbuds Gesture Capture to Support Wearable Interaction
Bibliographic record
Abstract
Due to the proliferation of smart wearables, it is now the case that designers can explore novel ways that devices can be used in combination by end-users. In this paper, we explore the gestural input enabled by the combination of smart earbuds coupled with a proximal smartwatch. We identify a consensus set of gestures and a taxonomy of the types of gestures participants create through an elicitation study. In a follow-on study conducted on Amazon's Mechanical Turk, we explore the social acceptability of gestures enabled by watch+earbud gesture capture. While elicited gestures continue to be simple, discrete, in-context actions, we find that elicited input is frequently abstract, varies in size and duration, and is split almost equally between on-body, proximal, and more distant actions. Together, our results provide guidelines for on-body, near-ear, and in-air input using earbuds and a smartwatch to support gesture capture.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".