Analyzing multidimensional movement interaction with generalized cross-wavelet transform

TL;DR

This paper introduces a generalized cross-wavelet transform method for analyzing complex, multidimensional, and nonstationary movement interactions in dance, enabling detailed frequency-wise and directional synchronization analysis.

Contribution

It extends the cross-wavelet transform to multidimensional signals, allowing comprehensive analysis of multidirectional and plurifrequential movement interactions in dance.

Findings

Effective in identifying interaction and leader-follower dynamics across body parts.

Quantifies the contribution of individual movements to overall synchrony.

Validated with simulated and real dance movement data.

Abstract

Humans can synchronize with musical events whilst coordinating their movements with others. Interpersonal entrainment phenomena, such as dance, involve multiple body parts and movement directions. Along with being multidimensional, dance movement interaction is plurifrequential, since it can occur at different frequencies simultaneously. Moreover, it is prone to nonstationarity, due to, for instance, displacements around the dance floor. Various methodological approaches have been adopted to study entrainment, but only spectrogram-based techniques allow for an integral analysis thereof. This article proposes an alternative approach based upon the cross-wavelet transform, a technique for nonstationary and plurifrequential analysis of univariate interaction. The presented approach generalizes the cross-wavelet transform to multidimensional signals. It allows to identify, for different frequencies of movement, interaction estimates of interaction and leader-follower dynamics across body parts and movement directions. Further, the generalized cross-wavelet transform can be used to quantify the frequency-wise contribution of individual body parts and movement directions to overall synchrony. The article provides a thorough mathematical description of the method and includes proofs of its invariance under translation, rotation, and reflection. Finally, its properties and performance are illustrated via examples using simulated data and behavioral data collected through a mirror game task and a free dance movement task.