Classification of phases in human motions by neural networks and hidden Markov models

A proper modeling of human motions plays a crucial role for many motion processing tasks. In particular, models for the automatic classification of elementary motion phases are highly important for the interaction between man and machine. In this work, we present different approaches for this modeling task based on neural networks and hidden Markov models. Both approaches yield reliable classification results. We show that even simple instances of the models work well if proper motion features are determined. A comparison of the different approaches shows the reasons for this behavior and leads to essential consequences for further modeling approaches.