Quantifying Coordination in Human Dyads via a Measure of Verticality

Abstract

Working towards the goal of understanding complex, interactive movement in human dyads, this paper presents a model for analyzing motion capture data of human pairs and proposes measures that correlate with features of the coordination in the movement. Based on deep inquiry of what it means to partner in a motion task, a measure that characterizes the changing verticality of each agent is developed. In parallel a naïve human motion expert provides a qualitative description of the features and quality of coordination within a dyad. Analysis on the verticality measure, the cross-correlation of verticality signals, and deviation of those verticality signals from the trend over time, provides quantitative insight that corroborates the naïve expert's analysis. Specifically, the paper shows that, for four samples of dyadic behavior, these measures provide information about 1) whether two agents were involved in the same dyadic interaction and 2) the level of "resistance" found in these interactions. Future work will test this model over a larger dataset and develop human-robot coordination schemes based on this model.