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Springer Tracts in Advanced Robotics
This paper describes a novel approach for incremental learning of motion pattern primitives through long-term observation of human motion. Human motion patterns are abstracted into a stochastic model representation, which can be used for both subsequent motion recognition and generation. The model size is adaptable based on the discrimination requirements in the associated region of the current knowledge base. As new motion patterns are observed, they are incrementally grouped together based ondoi:10.1007/978-3-642-14743-2_8 fatcat:5uqepixe6baqxiiw3lbs5go7ve