A Virtual Reality Dance Self-learning Framework using Laban Movement Analysis
Bibliographic record
Abstract
The capabilities of general motion evaluation algorithms are significantly limited in analyzing the stylistic qualities and expressions of dance movement.This study proposes a novel dance self-learning framework on the basis of the principles of Laban movement analysis (LMA) to facilitate trainees in automatically analyzing dance movements and correcting dance techniques without an expert.First, a "shape-effort" feature description model was presented in this framework to reflect the subtleties of dance movement.The evaluation of body-shape performance was obtained via open-end dynamic time warping algorithm.Next, rhythm was qualitatively assessed by curve fitting, whereas effort was measured by using standard deviation.Finally, constructive instructions were generated in this framework on basis of the assessment scores of the movement of the trainees.The framework was implemented in cave automatic virtual environment, and its effectiveness and feasibility were verified through experiments.Results demonstrate that the feature description model with 23 LMA parameters can be used in describing dance movements.Multi-mode feedback with direct instructions for the problems in question satisfies the learning habits of the trainee.The quality of the trainees' movements achieves an average of 10% overall improvement by using the framework.Body-shape performance acquires the most improvement of 18%, followed by effort.This study provides a new research method for evaluation and training of dance movements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".