Supervised Multiple Timescale Recurrent Neuron Network Model for Human Action Classification.

Multiple time-scales recurrent neural network MTRNN model is a useful tool to record and regenerate a continuous signal for a dynamic task. However, the MTRNN itself cannot classify different motions because there are no output nodes for classification tasks. Therefore, in this paper, we propose a novel supervised model called supervised multiple time-scales recurrent neural network SMTRNN to handle the classification issue. The proposed SMTRNN can label different kinds of signals without setting the initial states. SMTRNN provided both prediction and classification signals simultaneously during testing. In addition, the experiment results show that SMTRNN successfully classifies a continuous signal including multiple kinds of actions as well predicts motions.