Human activity recognition from skeleton poses
This work addresses the need for accurate human activity recognition in human-robot interaction, but it is incremental as it compares existing methods without introducing new techniques.
The paper compared classic algorithms (SVM, k-NN) with hierarchical neural gas approaches (GWR, GNG) for human activity recognition from skeleton poses, finding that neural gas methods achieved higher accuracy, with GWR reaching up to 92% on indoor home environment datasets.
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.