Technical Note: Quantifying music-dance synchrony with the application of a deep learning-based 2D pose estimator
Bibliographic record
Abstract
Abstract Dance interventions are more effective at improving gait and balance outcomes than other rehabilitation interventions. Repeated training may culminate in superior motor performance compared to other interventions without synchronization. This technical note will describe a novel method using a deep learning-based 2D pose estimator: OpenPose, alongside beat analysis of music to quantify movement-music synchrony during salsa dancing. This method has four components: i) camera setup and recording, ii) tempo/downbeat analysis and waveform cleanup, iii) OpenPose estimation and data extraction, and iv) synchronization analysis. Two trials were recorded: one in which the dancer danced synchronously to the music and one where they did not. The salsa dancer performed a solo basic salsa step continuously for 90 seconds to a salsa track while their movements and the music were recorded with a webcam. This data was then extracted from OpenPose and analyzed. The mean synchronization value for both feet was significantly lower in the synchronous condition than the asynchronous condition, indicating that this is an effective means to track and quantify a dancer’s movement and synchrony while performing a basic salsa step.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".