Classification of human actions into dynamics based primitives with application to drawing tasks

We develop the study of primitives of human motion, which we refer to as movemes. The idea is to understand human motion by decomposing it into a sequence of elementary building blocks that belong to a known alphabet of dynamical systems. How can we construct an alphabet of movemes from human data? In this paper we address this issue by introducing the notion of well-posednes. Using examples from human drawing data, we show that the well-posedness notion can be applied in practice so to establish if sets of actions, viewed as signals in time, can define movemes.