Trajectory Analysis on Spherical Self-Organizing Maps With Application to Gesture Recognition
Bibliographic record
Abstract
In this work, a new approach to gesture recognition using the properties of Spherical Self- Organizing Map (SSOM) is investigated. Bounded mapping of data onto a SSOM creates not only a powerful tool for visualization but also for modeling spatiotemporal information of gesture data. The SSOM allows for the automated decomposition of a variety of gestures into a set of distinct postures. The decomposition naturally organizes this set into a spatial map that preserves associations between postures, upon which we formalize the notion of a gesture as a trajectory through learned posture space. Trajectories from different gestures may share postures. However, the path traversed through posture space is relatively unique. Different variations of posture transitions occurring within a gesture trajectory are used to classify new unknown gestures. Four mechanisms for detecting the occurrence of a trajectory of an unknown gesture are proposed and evaluated on two data sets involving both hand gestures (public sign language database) and full body gestures (Microsoft Kinect database collected in-house) showing the effectiveness of the proposed approach.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".