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Sensors 2018, 18(9), 3117; https://doi.org/10.3390/s18093117

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

Psychology Department, Center for Biomedicine Imaging and Modeling, Computer Science Department, Rutgers Center for Cognitive Science, Rutgers University, New Brunswick, NJ 08854, USA
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Received: 13 July 2018 / Revised: 6 September 2018 / Accepted: 14 September 2018 / Published: 15 September 2018
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
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Abstract

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. View Full-Text
Keywords: sensor grids; weighted directed graphs; network connectivity; dyadic coordination; cohesiveness; spontaneous synergies; motor reciprocity; stochastic signatures; ballet partnering sensor grids; weighted directed graphs; network connectivity; dyadic coordination; cohesiveness; spontaneous synergies; motor reciprocity; stochastic signatures; ballet partnering
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Kalampratsidou, V.; Torres, E.B. Peripheral Network Connectivity Analyses for the Real-Time Tracking of Coupled Bodies in Motion. Sensors 2018, 18, 3117.

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