Categorization and learning of pen motion using hidden Markov models
Bibliographic record
Abstract
In this paper we present a framework for the classification and segmentation of motion data. First, a representation of different two-dimensional motion categories is proposed. Secondly, a system to categorize and segment motion is presented based on hidden Markov models, commonly used in speech recognition. Input to the system consists of online pen stroke data which includes the x, y position and time of each point along the line. Using derived speed and direction information the system classifies and segments the input into particular categories of motion. The resulting categorical information may be then used to describe the scene, extrapolate events, or as a part of a gesture recognition system. Applications beyond pen-based input are discussed. This paper contributes to pen based motion recognition research in two ways. First, a classification is performed based on a continuous sequence of observations, rather then feature extraction. Secondly, pen motion is transformed into a translation and rotation invariant representation prior to classification.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".