Variability of Handwriting Biomechanics: A Focus on Grip Kinetics during Signature Writing
Bibliographic record
Abstract
Grip kinetics are emerging as an important measure in clinical assessments of handwriting pathologies and fine motor rehabilitation as well as in biometric and forensic applications. The signature verification literature in particular has extensively examined the spatiotemporal, kinematic, and axial pressure characteristics of handwriting, but has minimally considered grip kinetics. Therefore, the focus of this thesis was to investigate the variability of grip kinetics in adults during signature writing. To address this goal, a database of authentic and well-practiced bogus signatures were collected with an instrumented pen that recorded the forces applied to its barrel. Four different analytical studies were conceived. The first study investigated the intra- and inter-participant variability of grip kinetic topography on the pen barrel based on authentic signatures written over 10 days. The main findings were that participants possessed unique grip force topographies even when the same grasp pattern was employed and that participants could be discriminated from each other with an average error rate of 1.2% on the basis of their grip force topographies. The second study examined the stability of different grip kinetic features over an extended period of a few months. The analyses revealed that intra-participant variation was generally much smaller than inter-participant variations even in the long term. In the third study, grip kinetics associated with authentic and well-practiced bogus signatures were compared. Differences in grip kinetic features between authentic and bogus signatures were only observed in a few participants. The kinetics of bogus signatures were not necessarily more variable. The variation of grip kinetic profiles between participants writing the same bogus signature was evaluated in the fourth study and an average error rate of 5.8% was achieved when verifying signatures with kinetic profile-based features. Collectively, the findings of this thesis serve to inform future applications of grip kinetic measures in biometric, clinical and industrial applications.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".