Detecting finger gestures with a wrist worn piezoelectric sensor array
Bibliographic record
Abstract
In this paper, we demonstrate a novel approach for the use of piezoelectric pressure sensors on a wearable wrist band to detect the occurrence of individual finger gestures. The system is designed to be wearer independent and require no training for the wearer or the system to perform accurate gesture detection. We used continuous signal windowing to identify bulk changes of several signal features before, during, and after a gesture was made by the wearer. For a range of window lengths and two filtering choices, a thresholding method was applied to the signals to determine the occurrence of a gesture. The error rate for missed and incorrectly labelled gestures were calculated for unfiltered and filtered data, as well as for various window lengths and thresholds. We found that longer window lengths resulted in fewer errors, that the maximum value within a window was a good indicator of whether an event occurred, and that thresholds needed to be sufficiently greater than the signal average, but too large a threshold value leads to missed events.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".