THE GENERATION OF VELOCITY PROFILES WITH AN ARTIFICIAL SIMULATOR
Bibliographic record
Abstract
A few years ago, a Kinematic Theory was proposed to analyze rapid human movements. The theory is based on a delta-lognormal equation which can be used to globally describe the basic properties of velocity profiles using seven parameters. This realistic model has been of great use to solve pattern recognition problems (signature verification, handwriting analysis and segmentation, etc.). To go further in that direction, a better understanding of the model is a prerequisite. This can be either in the context of psychophysical studies involving human subjects or in the context of computer simulations. In this paper, we use the same model form to develop a simulator that generates human-like velocity profiles. A basic subsystem model is both proposed and constructed with a Simulink Matlab tool; then many of these are connected to create an artificial neuromuscular network. Combining two networks in parallel, one agonist and the other antagonist, a synergy simulator is constructed. The similarity of the velocity patterns produced by the simulator is analyzed using a delta-lognormal parameter extractor. It is shown that the parameters extracted from artificially generated profiles vary in the same intervals as those of experimental profiles produced by human subjects. In future works the simulator tool will be used to study the control of rapid human movements.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".