THE MORPHOLOGY OF HUMAN ACTIONS: FINDING ESSENTIAL ACTUATORS, MOTION PATTERNS, AND THEIR COORDINATION

Abstract

In this paper, we present the steps required for the construction of a praxicon, a structured lexicon of human actions, through the learning of grammar systems for human actions. The discovery of a Human Activity Language involves learning the syntax of human motion which requires the construction of this praxicon. The morphology inference process assumes that a non-arbitrary symbolic representation of the human movement is given. Thus, to analyze the morphology of a particular action, we are given a symbolic representation for the motion of each actuator associated with several repeated performances of this action. As a formal model, we propose a new Parallel Synchronous Grammar System where each component grammar corresponds to an actuator. We present a novel parallel learning algorithm to induce this grammar system. Our representation explicitly contains the set of joints (degrees of freedom) actually responsible for achieving the goal aimed by the activity, the motion performed by each participating actuator, and the synchronization rules modeling coordination among these actuators. We evaluated our inference approach with synthetic data and real human motion data. The algorithm manages to induce the correct grammar system even when the input contains noise. Therefore, our approach was successful in both representational and learning aspects, and may serve as a tool to parse movement, learn patterns, and to generate actions.