  Human Action Recognition is an important task of Human Robot Interaction as
cooperation between robots and humans requires that artificial agents recognise
complex cues from the environment. A promising approach is using trained
classifiers to recognise human actions through sequences of skeleton poses
extracted from images or RGB-D data from a sensor. However, with many different
data-sets focused on slightly different sets of actions and different
algorithms it is not clear which strategy produces highest accuracy for indoor
activities performed in a home environment. This work discussed, tested and
compared classic algorithms, namely, support vector machines and k-nearest
neighbours, to 2 similar hierarchical neural gas approaches, the growing when
required neural gas and the growing neural gas.
