  Human recognition of the actions of other humans is very efficient and is
based on patterns of movements. Our theoretical starting point is that the
dynamics of the joint movements is important to action categorization. On the
basis of this theory, we present a novel action recognition system that employs
a hierarchy of Self-Organizing Maps together with a custom supervised neural
network that learns to categorize actions. The system preprocesses the input
from a Kinect like 3D camera to exploit the information not only about joint
positions, but also their first and second order dynamics. We evaluate our
system in two experiments with publicly available data sets, and compare its
performance to the performance with less sophisticated preprocessing of the
input. The results show that including the dynamics of the actions improves the
performance. We also apply an attention mechanism that focuses on the parts of
the body that are the most involved in performing the actions.
