Long Term Consistency of Handwriting Grip Kinetics in Adults
Bibliographic record
Abstract
While there is growing interest in clinical applications of handwriting grip kinetics, the consistency of these forces over time is not well-understood at present. In this study, we investigated the short- and long-term intra-participant consistency and inter-participant differences in grip kinetics associated with adult signature writing. Grip data were collected from 20 adult participants using a digitizing tablet and an instrumented pen. The first phase of data collection occurred over 10 separate days within a three week period. To ascertain long-term consistency, a second phase of data collection followed, one day per month over several months. In both phases, data were collected three times a day. After pre-processing and feature extraction, nonparametric statistical tests were used to compare the within-participant grip force variation between the two phases. Participant classification based on grip force features was used to determine the relative magnitude of inter-participant versus intra-participant differences. The misclassification rate for the longitudinal data were used as an indication of long term kinetic consistency. Intra-participant analysis revealed significant changes in grip kinetic features between the two phases for many participants. However, the misclassification rate, on average, remained stable, despite different demarcations of training, and testing data. This finding suggests that while signature writing grip forces may evolve over time, inter-participant kinetic differences consistently exceeds within-participant force changes in the long-term. These results bear implications on the collection, modeling and interpretation of grip kinetics in clinical 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 | 0.004 | 0.028 |
| 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".