Analyzing multidimensional movement interaction with generalized cross-wavelet transform
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
Published inHuman Movement Science
DisciplineSecure Communications Engineering and Signal ProcessingMusiikkitiedeMusic, Mind and TechnologyOhjelmisto- ja tietoliikennetekniikkaSecure Communications Engineering and Signal ProcessingMusicologyMusic, Mind and TechnologySoftware and Communications Engineering
© 2021 The Authors. Published by Elsevier B.V.
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. ...
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Related funder(s)Academy of Finland
Funding program(s)Academy Project, AoF; Postdoctoral Researcher, AoF
Additional information about fundingThis work was supported by funding from the Academy of Finland (project numbers 332331 and 314651).
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