A1 Journal article (refereed)
Analyzing multidimensional movement interaction with generalized cross-wavelet transform (2022)

Toiviainen, P., & Hartmann, M. (2022). Analyzing multidimensional movement interaction with generalized cross-wavelet transform. Human Movement Science, 81, Article 102894. https://doi.org/10.1016/j.humov.2021.102894

JYU authors or editors

Publication details

All authors or editors: Toiviainen, Petri; Hartmann, Martín

Journal or series: Human Movement Science

ISSN: 0167-9457

eISSN: 1872-7646

Publication year: 2022

Volume: 81

Article number: 102894

Publisher: Elsevier

Publication country: Netherlands

Publication language: English

DOI: https://doi.org/10.1016/j.humov.2021.102894

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/78685

Web address of parallel published publication (pre-print): https://arxiv.org/abs/2104.09783

Abstract

Humans are able to 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 for the study of human 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 state-of-the-art 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, 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 movement synchrony. Since both in- and anti-phase relationships are dominant modes of coordination, the proposed implementation ignores whether movements are identical or opposite in phase. 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 four examples using simulated data and behavioral data collected through a mirror game task and a free dance movement task.

Keywords: music; dance (performing arts); social interaction; sense of rhythm; synchronizing; motion; motion analysis; signal processing

Free keywords: Entrainment; Joint action; Dyadic interaction; Leader-follower dynamics; Time-frequency analysis

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Ministry reporting: Yes

Reporting Year: 2022

Preliminary JUFO rating: 1