Eliciting Wrist and Finger Gestures to Guide Recognizer Design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While hand gestures, i.e. movements of the fingers and wrist, are a low-effort input modality, sensing and recognition of these smallscale gestures is challenging. In particular, while many authors have explored varying designs of hardware to support hand gesture input, each systems recognize their own gesture set, rendering challenging comparisons between different capture and recognition systems. In this paper, we explore the design of hand and finger gesture input by conducting an elicitation study to understand the tradeoffs between hand, wrist, and arm gestures. Alongside this, to evaluate the overall potential of wrist-worn recognition, we explore the design of hardware to recognize gestures by contrasting an IMUonly recognizer with a simple low-cost wrist-flex sensor. We discuss the implications of our work both to the comparative evaluation of systems and to the design of enhanced hardware sensing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it