Next Article in Journal
Energy Efficient Real-Time Scheduling Using DPM on Mobile Sensors with a Uniform Multi-Cores
Previous Article in Journal
Optimal Multi-Type Sensor Placement for Structural Identification by Static-Load Testing
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(12), 2907;

Centralized Networks to Generate Human Body Motions

Institute for Mechanical Engineering Problems, 195251 Saint Petersburg, Russia
Mechanics and Optics, Saint Petersburg National Research University of Information Technologies, 191119 Saint Petersburg, Russia
DIMNP-UMR 5235 CNRS/UM, University of Montpellier, 34095 Montpellier, France
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)
Full-Text   |   PDF [4753 KB, uploaded 14 December 2017]   |  


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

Figure 1

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).

Share & Cite This Article

MDPI and ACS Style

Vakulenko, S.; Radulescu, O.; Morozov, I.; Weber, A. Centralized Networks to Generate Human Body Motions. Sensors 2017, 17, 2907.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top