VibRing: A Wearable Vibroacoustic Sensor for Single-Handed Gesture Recognition
Bibliographic record
Abstract
Single-handed gestures offer rapid and intuitive interactions for input in interactive applications ranging from smartwatches and phones to augmented reality. Past research has explored using computer vision or inertial measurement units (IMUs) to sense such gestures, but these sensing modalities can be variously subject to occlusion, high power consumption, or sensitivity to random motion. In this work, we explore passively detecting the vibroacoustic signature of subtle single-handed gestures through a wearable piezoelectric sensor, providing a robust, low-power sensing modality. We present (1) a hand-gesture design framework encompassing a large set of subtle, rapid single-handed gestures which balance comfort and vibroacoustic distinguishability, (2) VibRing, a lightweight wireless hand-gesture sensing platform, leveraging a single finger-worn vibroacoustic sensor, and (3) a multifaceted system evaluation where we consider several aspects - general usability, tolerance to variance, user adaptability, and extended usage. Our results demonstrate that VibRing can support an 11-gesture set with a general accuracy of \(94.2\%\) and low-performance variance across multiple days ( \(90.2\%\) accuracy in cross-day validation). To support a new user, VibRing requires only 10 minutes of training data to achieve an accuracy of \(92.7\%\) . We also tested the extended use of VibRing in an office study where users performed periodic gesture inputs during typical office tasks with real-time classification, achieving a true-positive rate of \(90.9\%\) . Finally, to demonstrate the utility of VibRing, we present three examples of applications which benefit from our subtle gesture interactions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".