Advertisement

Experimental Brain Research

, Volume 123, Issue 4, pp 474–480 | Cite as

Simple artificial neural network models can generate basic muscle activity patterns for human locomotion at different speeds

  • S. D. Prentice
  • A. E. Patla
  • D. A. Stacey
Research Note

Abstract

 A neural network model has been developed to represent the shaping function of a central pattern generator (CPG) for human locomotion. The model was based on cadence and electromyographic data obtained from a single human subject who walked on a treadmill. The only input to the model was the fundamental timing of the gait cycle (stride rate) in the form of sine and cosine waveforms whose period was equal to the stride duration. These simple signals were then shaped into the respective muscle activation patterns of eight muscles of the lower limb and trunk. A network with a relatively small number of hidden units trained with back-propagation was able to produce an excellent representation of both the amplitude and timing characteristics of the EMGs over a range of walking speeds. The results are further discussed with respect to the dependence of some muscles upon sensory feedback and other inputs not explicitly presented to the model.

Key words Central pattern generator Locomotion Electromyography Neural network 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • S. D. Prentice
    • 1
  • A. E. Patla
    • 1
  • D. A. Stacey
    • 2
  1. 1.Neural Control Laboratory, Department of Kinesiology, University of Waterloo, Waterloo, Canada, N2L 3G1CA
  2. 2.Department of Computer and Information Science, University of Guelph, Guelph, Canada, N1G 2W1CA

Personalised recommendations