Automatic and perceptual assessment of motion coordination in dyadic dance
Résumé
Free dyadic dance is an ecological context in which people spon- taneously synchronize and imitate each other. Yet, automatically measuring these mutual behavioral adjustments remains challeng- ing. Here, we compare a data-driven approach with human per- ception for detecting motion Interpersonal coordination (IC) in dyadic dance. Using a motion capture dataset where visual and au- ditory coupling between dancers was experimentally manipulated, we formulated binary classification tasks contrasting coupled and uncoupled partners. We benchmarked machine learning models for multivariate time series classification. Then we compared their predictions with perceptual judgments from an online experiment (N = 87). Machine learning models are evaluated on two types of input data: raw velocities and features based on the synchrony of pairwise sensors across the two dancers. Results show that models’ performance improved significantly when using synchrony features over raw velocities. Humans reached scores similar to models using raw velocities(≃60% accuracy), with substantial inter-agreements (≃70%). Correlational analysis with variables derived from the mu- tual quantity of motions (QoM) of dancers revealed a human bias toward judging partners with similar QoM as coupled.
Domaines
| Origine | Fichiers éditeurs autorisés sur une archive ouverte |
|---|---|
| Licence |
