Organization of behavioral knowledge from extraction of temporal-spatial features of human whole body motions
Why this work is in the frame
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Bibliographic record
Abstract
This paper describes an approach to structuring behavioral knowledge based on classification of human whole body motions and extraction of the behavioral transitions. The motion patterns are learned by Hidden Markov Models (HMMs), which can be used for classification of the motion patterns. The HMMs are called “motion symbol” since They abstract their corresponding motion patterns. The motion patterns are organized into a hierarchical tree structure (“motion symbol tree”) representing the property of similarity among the motion patterns. The motion patterns are classified based on the motion symbol tree. Concatenated sequences of motion patterns are stochastically represented as transitions between the abstracted motion patterns by using an N-gram Model (“motion symbol graph”), and the transitional relationships of the human behaviors are extracted. The integration of the motion symbol tree and the motion symbol graph makes it possible to recognize motion patterns fast and predict human behavior during observation. The experiments on a large motion dataset validate the proposed framework.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 it