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Technical Note: Quantifying music-dance synchrony during salsa dancing with a deep learning-based 2D pose estimator.

Filip Potempski1, Andrea Sabo2, Kara K Patterson3

  • 1KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, Canada; Department of Physical Therapy, University of Toronto, Toronto, Canada; Department of Arts & Sciences, University of Toronto, Toronto, Canada; Department of Applied Health Sciences, Brock University, St. Catherines, Canada.

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Summary
This summary is machine-generated.

This study introduces a new method to measure how well dancers sync their movements with music using AI pose estimation. The findings suggest this technique can effectively quantify movement-music synchrony, aiding dance research.

Keywords:
DancingHuman pose tracking algorithmMotion analysisOpenPoseSynchronization

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Area of Science:

  • Neuroscience
  • Kinesiology
  • Computer Science

Background:

  • Dance interventions show potential for enhancing gait and balance in neurological conditions.
  • Movement-music synchrony is a hypothesized mechanism behind dance's physical benefits.
  • Quantifying this synchrony is crucial for understanding dance's therapeutic effects.

Purpose of the Study:

  • To present a novel method for quantifying movement-music synchrony during salsa dancing.
  • To utilize deep learning (OpenPose) and music beat analysis for this quantification.
  • To assess the method's effectiveness in distinguishing between on-beat and off-beat dancing.

Main Methods:

  • A four-component method involving camera setup, music analysis, OpenPose estimation, and synchrony analysis.
  • Recording participants (n=4) performing a basic salsa step for 90 seconds.
  • Comparing conditions where participants danced on-beat versus off-beat to the music.

Main Results:

  • The developed method successfully quantified movement-music synchrony.
  • Median asynchrony values differed between participants with and without dance training.
  • Significant differences in asynchrony were observed between on-beat and off-beat conditions.

Conclusions:

  • The described method provides an effective means to quantify movement-music synchrony in dancers.
  • This approach can differentiate between varying levels of synchrony and dance experience.
  • Further research can leverage this method to explore dance's impact on neurological conditions.