On the Need for Accurate Brushstroke Segmentation of Tablet-Acquired Kinematic and Pressure Data: The Case of Unconstrained Tracing
Bibliographic record
Abstract
Brushstroke segmentation algorithms are critical in computer-based analysis of fine motor control via handwriting, drawing, or tracing tasks. Current segmentation approaches typically rely only on one type of feature, either spatial, temporal, kinematic, or pressure. We introduce a segmentation algorithm that leverages both spatiotemporal and pressure features to accurately identify brushstrokes during a tracing task. The algorithm was tested on both a clinical and validation dataset. Using validation trials with incorrectly identified brushstrokes, we evaluated the impact of segmentation errors on commonly derived biomechanical features used in the literature to detect graphomotor pathologies. The algorithm exhibited robust performance on validation and clinical datasets, effectively identifying brushstrokes while simultaneously eliminating spurious, noisy data. Spatial and temporal features were most affected by incorrect segmentation, particularly those related to the distance between brushstrokes and in-air time, which experienced propagated errors of 99% and 95%, respectively. In contrast, kinematic features, such as velocity and acceleration, were minimally affected, with propagated errors between 0 to 12%. The proposed algorithm may help improve brushstroke segmentation in future studies of handwriting, drawing, or tracing tasks. Spatial and temporal features derived from tablet-acquired data should be considered with caution, given their sensitivity to segmentation errors and instrumentation characteristics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".