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<p>This study presents a method for quantifying bodily coordination between two people using motion capture data recorded during unscripted, face-to-face interactions. The approach enables the examination of the magnitude, temporal dynamics, and morphological characteristics of coordination patterns across multiple dyads, while allowing participants to move and speak freely. It provides a continuous analysis of the full movement stream without requiring segmentation or predefined coding. Technically, the method relies on speed signals derived from position or acceleration data, which are well suited for cross-correlation analyses. By computing cross-correlations between the speed signals of interacting partners, it produces a single curve that captures both immediate and time-lagged coordination, aggregated across all dyads in a given condition. Confidence intervals are then used to statistically compare coordination patterns between experimental groups. The article outlines a step-by-step procedure for implementing this method using three-dimensional motion capture datasets.</p>

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Analyzing interpersonal coordination through motion capture and cross-correlation: An integrated approach to magnitude, temporality, and morphology

  • 期刊论文

  • 发表日期：

    2026年5月11日

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  • Esteban Hurtado,
  • Marisol Correa,
  • Zamara Cuadros,
  • Javiera Paredes,
  • David Carré,
  • Carlos Cornejo

摘要

This study presents a method for quantifying bodily coordination between two people using motion capture data recorded during unscripted, face-to-face interactions. The approach enables the examination of the magnitude, temporal dynamics, and morphological characteristics of coordination patterns across multiple dyads, while allowing participants to move and speak freely. It provides a continuous analysis of the full movement stream without requiring segmentation or predefined coding. Technically, the method relies on speed signals derived from position or acceleration data, which are well suited for cross-correlation analyses. By computing cross-correlations between the speed signals of interacting partners, it produces a single curve that captures both immediate and time-lagged coordination, aggregated across all dyads in a given condition. Confidence intervals are then used to statistically compare coordination patterns between experimental groups. The article outlines a step-by-step procedure for implementing this method using three-dimensional motion capture datasets.