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Sensors 2017, 17(12), 2907; https://doi.org/10.3390/s17122907

Centralized Networks to Generate Human Body Motions

1
Institute for Mechanical Engineering Problems, 195251 Saint Petersburg, Russia
2
Mechanics and Optics, Saint Petersburg National Research University of Information Technologies, 191119 Saint Petersburg, Russia
3
DIMNP-UMR 5235 CNRS/UM, University of Montpellier, 34095 Montpellier, France
4
Computer Science Department, University of Bonn, 53113 Bonn, Germany
*
Author to whom correspondence should be addressed.
Received: 15 September 2017 / Revised: 10 December 2017 / Accepted: 11 December 2017 / Published: 14 December 2017
(This article belongs to the Section Sensor Networks)
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Abstract

We consider continuous-time recurrent neural networks as dynamical models for the simulation of human body motions. These networks consist of a few centers and many satellites connected to them. The centers evolve in time as periodical oscillators with different frequencies. The center states define the satellite neurons’ states by a radial basis function (RBF) network. To simulate different motions, we adjust the parameters of the RBF networks. Our network includes a switching module that allows for turning from one motion to another. Simulations show that this model allows us to simulate complicated motions consisting of many different dynamical primitives. We also use the model for learning human body motion from markers’ trajectories. We find that center frequencies can be learned from a small number of markers and can be transferred to other markers, such that our technique seems to be capable of correcting for missing information resulting from sparse control marker settings. View Full-Text
Keywords: neural networks; markers; human body motions; motion sensors; motion representation; motion reconstruction neural networks; markers; human body motions; motion sensors; motion representation; motion reconstruction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Vakulenko, S.; Radulescu, O.; Morozov, I.; Weber, A. Centralized Networks to Generate Human Body Motions. Sensors 2017, 17, 2907.

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