Variation of grip force profile during signature writing
Bibliographic record
Abstract
To examine the possibility of differentiating between individuals based on the total grip force profile generated while writing a signature, this study investigated the variability of features derived from grip force profiles of twenty adult participants writing the same signature over multiple sessions. Using an instrumented writing utensil and a digitizing tablet, each participant provided 600 samples of a well-practiced bogus signature over a period of 10 days. Using a linear discriminant analysis classifier with a combination of temporal, spectral and information-theoretic features, discrimination among participants was possible with an average misclassification rate of 5.8%. This linear classifier outperformed nonparametric and nonlinear classifier alternatives that were tested in this study. These results suggest the existence of a unique kinetic profile for each writer even when writing the same signature and despite some variation within each writer. The results of this study also highlight the potential of using grip kinetics as a biometric measure for signature verification applications.
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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 | 0.000 |
| 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 | 0.000 |
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".