We introduce applications of established methods in time-series and network
analysis that we jointly apply here for the kinematic study of gesture
ensembles. We define a gesture ensemble as the set of gestures produced
during discourse by a single person or a group of persons. Here we are
interested in how gestures kinematically relate to one another. We use
a bivariate time-series analysis called dynamic time warping to assess how
similar each gesture is to other gestures in the ensemble in terms of their
velocity profiles (as well as studying multivariate cases with gesture velocity
and speech amplitude envelope profiles). By relating each gesture event to
all other gesture events produced in the ensemble, we obtain a weighted
matrix that essentially represents a network of similarity relationships. We
can therefore apply network analysis that can gauge, for example, how
diverse or coherent certain gestures are with respect to the gesture ensemble.
We believe these analyses promise to be of great value for gesture
studies, as we can come to understand how low-level gesture features
(kinematics of gesture) relate to the higher-order organizational structures
present at the level of discourse.