Abstract
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.
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References
Arie, H., Arakaki, T., Sugano, S., Tani, J.: Imitating others by composition of primitive actions: A neuro-dynamic model. Robotics and Autonomous Systems 60(5), 729–774 (2012)
Cruz, L., Djalma, L., Luiz, V.: Kinect and RGBD Images: Challenges and Applications Graphics. In: 2012 25th SIBGRAPI Conference on Patterns and Images Tutorials (SIBGRAPI-T), August 22-25 (2012)
Doya, K., Yoshizawa, S.: Adaptive neural oscillator using continuous-time back-propagation learning. Neural Network 2, 375–386 (1989)
Funahashi, K., Nakamura, Y.: Approximation of dynamical systems by continuous timerecurrent neural networks. Neural Networks 6(6), 801–806 (1993)
Hinoshita, W., Arie, H., Tani, J., Okuno, H.G., Ogata, T.: Emergence of hierarchical structure mirroring linguistic composition in a recurrent neural network. Neural Networks 24(4), 311–320 (2011)
Husken, M., Stagge, P.: Recurrent Neural Networks for Time Series Classification. Neuro Computing 50(C), 223–235 (2003)
Jeong, S., Arie, H., Lee, M., Tani, J.: ∙Neuro-robotics study on integrative learning of proactive visual attention and motor behaviors. Cogn. Neurodyn. 6, 43–59 (2011, 2012)
Joslin, C., El-Sawah, A., Chen, Q., Georganas, N.: Dynamic Gesture Recognition. In: IMTC 2005 – Instrumentation and Measurement Technology Conference, Ottawa, Canada, May 17-19 (2005)
Poppe, R.: A survey on vision-based human action recognition. Image and Vision Computing 28(6), 976–990 (2010)
Sakai, K., Kitaguchi, K., Hikosaka, O.: Chunking during human visuomotor sequence learning. Exp. Brain Res. 152, 229–242 (2003)
Tani, J., Nishimoto, R., Paine, R.: Achieving “organic compositionality” throughself-organization: reviews on brain-inspired robotics experiments. NeuralNetworks 21, 584–603 (2008)
Thomas, K., Andre, B., Michalis, F., KcC: HMM-based Human Motion Recognition with Optical Flow Data. In: 9th IEEE-RAS International Conference on Humanoid Robot, Paris, France, December 7-10 (2009)
Thoroughman, K.A., Shadmehr, R.: Learning of action through adaptive combination of motor primitives. Science 407, 742–747 (2000)
YamashitaY., T.J.: Emergence of functional hierarchy in a multiple timescaleneural network model: a humanoid robot experiment. PLoS ComputationalBiology 4(11) (2008)
Zhang, Y., Ogata, T., Takahashi, T., Okuno, H.G.: Dynamic Recognition of Environmental Sounds with Recurrent Neural Network. In: The 28th Annual Conference of the Robotics Society of Japan, Nagoya, Japan (2010)
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Yu, Z., Mallipeddi, R., Lee, M. (2013). Supervised Multiple Timescale Recurrent Neuron Network Model for Human Action Classification. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42042-9_25
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DOI: https://doi.org/10.1007/978-3-642-42042-9_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42041-2
Online ISBN: 978-3-642-42042-9
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