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Combining a 3D Reflex Based Neuromuscular Model with a State Estimator Based on Central Pattern Generators

  • T. J. H. BrugEmail author
  • F. Dzeladini
  • A. R. Wu
  • A. J. Ijspeert
Conference paper
Part of the Biosystems & Biorobotics book series (BIOSYSROB, volume 15)

Abstract

A neuromuscular model (NMC) presented by H. Geyer and extended by S. Song shows very interesting similarities with real human locomotion. The model uses a combination of reflex loops to generate stable locomotion and is able to cope with external disturbances and adapt to different conditions. However, to our knowledge no works exist on the capability of the model to handle sensory noise. In this paper, we present a method for designing Central Pattern Generators (CPG) as feedback predictors, which can be used to handle large amount of sensory noise. We show that the whole system (NMC + CPG) is able to cope with a very large amount of noise, much larger than what the original system (NMC) could handle.

Keywords

Kalman Filter Noise Variance Central Pattern Generator Robotic Device Velocity Sensor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The work presented here was performed as part of the SYMBITRON project which is supported by EU research program FP7, FET-Proactive initiative “Symbiotic human-machine interaction” (ICT-2013-10) under project contract #611626.

References

  1. 1.
    Yury Gerasimenko, Roland R Roy, and V Reggie Edgerton. Epidural stimulation: comparison of the spinal circuits that generate and control locomotion in rats, cats and humans. Experimental neurology 209(2), 417–425 (2008)CrossRefGoogle Scholar
  2. 2.
    S. Grillner, P. Zangger, On the central generation of locomotion in the low spinal cat. Experimental Brain Research 34(2), 241–261 (1979)CrossRefGoogle Scholar
  3. 3.
    A.J. Ijspeert, Central pattern generators for locomotion control in animals and robots: a review. Neural Netw. 21(4), 642–653 (2008)CrossRefGoogle Scholar
  4. 4.
    A.D. Kuo, The relative roles of feedforward and feedback in the control of rhythmic movements. Mot. Control-Champaign- 6(2), 129–145 (2002)Google Scholar
  5. 5.
    G.N. Orlovskiĭ, T.G. Deliagina, S. Grillner, Neuronal Control of Locomotion: From Mollusc to Man (Oxford University Press, 1999)Google Scholar
  6. 6.
    Hartmut Geyer, Hugh Herr, A muscle-reflex model that encodes principles of legged mechanics produces human walking dynamics and muscle activities. IEEE Trans. Neural Syst. Rehabil. Eng. 18(3), 263–273 (2010)CrossRefGoogle Scholar
  7. 7.
    S. Song, H. Geyer, A neural circuitry that emphasizes spinal feedback generates diverse behaviours of human locomotion. J. Phys. 593(16), 3493–3511 (2015)Google Scholar
  8. 8.
    F. Dzeladini, J. van den Kieboom, A. Ijspeert, The contribution of a central pattern generator in a reflex-based neuromuscular model (2014)Google Scholar
  9. 9.
    P.E. Kalman, A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1), 35–45 (1960)CrossRefGoogle Scholar
  10. 10.
    H.P. Clamann, Statistical analysis of motor unit firing patterns in a human skeletal muscle. Biophys. J. 9(10), 1233 (1969)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • T. J. H. Brug
    • 1
    Email author
  • F. Dzeladini
    • 2
  • A. R. Wu
    • 2
  • A. J. Ijspeert
    • 2
  1. 1.Department of Biomechanical EngineeringUniversity of TwenteEnschedeThe Netherlands
  2. 2.Biorobotics LaboratoryEcole Polytechnique Federale de LausanneLausanneSwitzerland

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