Graph dictionary learning for the study of human motion.

In this article, we introduce a method inspired by Graph Signal Processing (GSP) for the analysis of human motion based on the 3D positions of skeletal joints. Our approach uses a graph dictionary learning technique, in which each velocity sample is decomposed into a linear combination of a limited set of atoms acquired directly from the data. The efficacy of this methodology is evaluated using a dataset focused on upper limb elevations.