Extending The Sigma-Lognormal Model Of The Kinematic Theory To Three Dimensions
Bibliographic record
Abstract
The Kinematic Theory of rapid human movements and its Sigma-Lognormal model enables to model human gestures, in particular complex handwriting patterns such as words, signatures and free gestures. This paper investigates the extension of the theory and its Sigma-Lognormal model from two dimensions to three, taking into account new acquisition modalities (motion capture), multiple subjects, and unconstrained motions. Despite the increased complexity and the new acquisition modalities, we demonstrate that the Sigma-Lognormal model can be successfully generalized to describe 3D human movements. Starting from the 2D model, we replace circular with spherical motions to derive a representation of unconstrained human move- ments with a new 3D Sigma-Lognormal model. First experiments show a high reconstruction quality with an average signal-to-noise ratio (SNR) of 18.52 dB on the HDM05 dataset. Gesture recognition using dynamic time warping (DTW) achieves similar recognition accuracies when using original and reconstructed gestures, which confirms the high quality of the proposed model.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".