Incremental learning of human behaviors using hierarchical hidden Markov models

This paper proposes a novel approach for extracting a model of movement primitives and their sequential relationships during online observation of human motion. In the proposed approach, movement primitives, modeled as hidden Markov models, are autonomously segmented and learned incrementally during observation. At the same time, a higher abstraction level hidden Markov model is also learned, encapsulating the relationship between the movement primitives. For the higher level model, each hidden state represents a motion primitive, and the observation function is based on the likelihood that the observed data is generated by the motion primitive model. An approach for incremental training of the higher order model during online observation is developed. The approach is validated on a dataset of continuous movement data.