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

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.

Digitized ADOS: Social Interactions beyond the Limits of the Naked Eye

“…We can examine the activities for known physiological ranges up to 30 Hz, and/or beyond those ranges up to 64 Hz in this case, focusing on different frequency bands to examine different patterns of entrainment for different dyads, etc. (see other examples [24,37].) In this paper, we focus on the activities across all bands.…”

The Autonomic Nervous System Differentiates between Levels of Motor Intent and End Effector

“…At the motor control level, autonomous and spontaneous movements are important to develop a sense of action ownership in the face of motor redundancy [ 17 ]. Spontaneous motions can be covert, as those subtle motions occurring in a coma patient [ 18 ] or those occurring in a neonate [ 19 ]; or overt, as when they coexist with deliberate/staged ones, embedded in complex sports routines [ 13 , 14 ] and/or ballet choreographies [ 20 ]. Such complex overt movements require the coordination and control of many degrees of freedom (DoFs) across the body.…”

“…These internal sensations could help the brain differentiate contextual variations emerging from external environmental cues from sensory information that is internally self-generated by the nervous systems [ 13 , 14 ]. External information may include, for example, changes in visual and auditory inputs, such as shifts in lighting conditions, or modulations in sound and music [ 20 , 35 ].…”

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

“…For all modes of data, we could also examine the stochasticity of the times between peaks (instead of peak amplitude), which also generate time series. Other time series of parameters can be derived from such waveforms, and their MMS can be used to ascertain cohesiveness and connectivity from the network that was constructed 32,33,34 . Furthermore, these analyses can also be extended to the frequency domain 34 .…”

“…Other time series of parameters can be derived from such waveforms, and their MMS can be used to ascertain cohesiveness and connectivity from the network that was constructed 32,33,34 . Furthermore, these analyses can also be extended to the frequency domain 34 . In addition to the mutual information network analysis, we could have focused on other topological features of the network to differentiate PWP and controls and to stratify PWP.…”