Bibliographic record
Abstract
We propose a style of hand postures to trigger commands on a laptop. The key idea is to perform hand-postures while keeping the hands on, beside, or below the keyboard, to align with natural laptop usage. 36 hand-posture variations are explored considering three resting locations, left or right hand, open or closed hand, and three wrist rotation angles. A 30-participant formative study measures posture preferences and generates a dataset of nearly 350K images under different lighting conditions and backgrounds. A deep learning recognizer achieves over 97% accuracy when classifying all 36 postures with 2 additional non-posture classes for typing and non-typing. A second experiment with 20 participants validates the recognizer under real-time usage and compares posture invocation time with keyboard shortcuts. Results find low error rates and fast formation time, indicating postures are close to current typing and pointing postures. Finally, practical use case demonstrations are presented, and further extensions discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.043 |
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".