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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. Where do these dynamic primitives come from in practice? How can we construct an alphabet of movemes from human data? In this paper we address these issues. We define conditions under which collections of signals are well-posed according to a dynamical model class M and thus can generate movemes. Using examples from human drawing data, we show that the definition of well-posedness can be applied in practice so to establish if sets of actions, reviewed as signals in time, can define movemes.