Temporal Deep Belief Network for Online Human Motion Recognition

Interaction between humans and machines, like social robots, requires real time recognition of human actions. Most approaches to this problem wait for the end of the gesture to perform classification. In this paper we present a deep learning approach to online gesture recognition that allows for an estimation of the current gesture since its beginning. Our approach is to modify the existing Temporal Deep Belief Network (TDBN) architecture. The result is a Discriminative Temporal Deep Belief Network (DTDBN) which we apply to the online classification of motion capture streams. We optimize and evaluate our model in comparison with related work.