Department of Music, Art and Culture Studies, P.O.Box 35(M), 40014, University of Jyväskylä, Finland; Finnish Centre of Excellence in Music, Mind, Body and Brain, P.O. Box 35(M), 40014, University of Jyväskylä, Finland.
Hum Mov Sci. 2022 Feb;81:102894. doi: 10.1016/j.humov.2021.102894. Epub 2021 Nov 16.
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.
人类能够在协调与他人运动的同时与音乐事件同步。人际同步现象，如舞蹈，涉及多个身体部位和运动方向。舞蹈运动的相互作用不仅具有多维性，而且还具有多频性，因为它可以同时在不同频率发生。此外，由于在舞池周围的位移等原因，它容易出现非平稳性。已经采用了各种方法来研究人类的同步性，但只有基于频谱图的技术才能对其进行整体分析。本文提出了一种基于交叉小波变换的替代方法，该方法是一种用于分析非平稳和多频单变量相互作用的最新技术。所提出的方法将交叉小波变换推广到多维信号。它允许为不同的运动频率识别，在身体部位和运动方向上对交互和领导者-跟随者动态进行估计。此外，广义交叉小波变换可用于量化各个身体部位和运动方向对整体运动同步的频率贡献。由于同相和反相关系都是协调的主要模式，因此所提出的实现忽略了运动是否在相位上相同或相反。本文提供了该方法的详细数学描述，并证明了其在平移、旋转和反射下的不变性。最后，通过使用模拟数据和通过镜像游戏任务和自由舞蹈运动任务收集的行为数据的四个示例，说明了其性质和性能。
