Peripheral Network Connectivity Analyses for the Real-Time Tracking of Coupled Bodies in Motion

Dyadic interactions are ubiquitous in our lives, yet they are highly challenging to study. Many subtle aspects of coupled bodily dynamics continuously unfolding during such exchanges have not been empirically parameterized. As such, we have no formal statistical methods to describe the spontaneously...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2018-09, Vol.18 (9), p.3117
Main Authors: Kalampratsidou, Vilelmini, Torres, Elizabeth B
Format: Article
Language:English
Subjects:
Autism
ballet partnering
Behavior
Biological rhythms
Brain research
Cohesion
cohesiveness
Dance notation
dyadic coordination
Graph theory
Hypothesis testing
motor reciprocity
network connectivity
Performing arts
Real time
sensor grids
spontaneous synergies
Statistical methods
stochastic signatures
weighted directed graphs
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Summary:Dyadic interactions are ubiquitous in our lives, yet they are highly challenging to study. Many subtle aspects of coupled bodily dynamics continuously unfolding during such exchanges have not been empirically parameterized. As such, we have no formal statistical methods to describe the spontaneously self-emerging coordinating synergies within each actor's body and across the dyad. Such cohesive motion patterns self-emerge and dissolve largely beneath the awareness of the actors and the observers. Consequently, hand coding methods may miss latent aspects of the phenomena. The present paper addresses this gap and provides new methods to quantify the moment-by-moment evolution of self-emerging cohesiveness during highly complex ballet routines. We use weighted directed graphs to represent the dyads as dynamically coupled networks unfolding in real-time, with activities captured by a grid of wearable sensors distributed across the dancers' bodies. We introduce new visualization tools, signal parameterizations, and a statistical platform that integrates connectivity metrics with stochastic analyses to automatically detect coordination patterns and self-emerging cohesive coupling as they unfold in real-time. Potential applications of these new techniques are discussed in the context of personalized medicine, basic research, and the performing arts.
ISSN:1424-8220
1424-8220
DOI:10.3390/s18093117