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Improved identification of the human shoulder kinematics with muscle biological filters

  • Image and Signal Processing
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Book cover Artificial Intelligence in Medicine (AIME 1997)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1211))

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Abstract

In this paper, we introduce new refinements to the approach based on dynamic recurrent neural networks (DRNN) to identify, in humans, the relationship between the muscle electromyographic (EMG) activity and the arm kinematics during the drawing of the figure eight using an extended arm. This method of identification allows to clearly interpret the role of each muscle in any particular movement.

We show here that the quality and the speed of the complex identification process can be improved by applying some treatments to the input signals (i.e. raw EMG signals). These treatments, applied on raw EMG signals, help to get signals that are better reflections of muscle forces which are the real actuators of the movements.

J.P. Draye is also a Senior Research Assistant of the Belgian National Fund for Scientific Research (F.N.R.S.)

G. Cheron is also with the Department of Neurosciences at the Université de Mons-Hainaut (B7000 Mons — Belgium)

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Authors and Affiliations

  1. Laboratory Rue de Houdain, Polytechnical Faculty of Mons “Parallel Information Processing”, 9-B7000, Mons, Belgium

    Jean-Philippe Draye, Davor Pavisic & Gaëtan Libert

  2. Laboratory of Biomechanics, Free University of Brussels, Avenue Heger, 28-B1050, Brussels, Belgium

    Guy Cheron

Authors
  1. Jean-Philippe Draye
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  2. Guy Cheron
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  3. Davor Pavisic
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  4. Gaëtan Libert
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Editor information

Elpida Keravnou Catherine Garbay Robert Baud Jeremy Wyatt

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© 1997 Springer-Verlag Berlin Heidelberg

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Draye, JP., Cheron, G., Pavisic, D., Libert, G. (1997). Improved identification of the human shoulder kinematics with muscle biological filters. In: Keravnou, E., Garbay, C., Baud, R., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIME 1997. Lecture Notes in Computer Science, vol 1211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0029475

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  • DOI: https://doi.org/10.1007/BFb0029475

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-62709-8

  • Online ISBN: 978-3-540-68448-0

  • eBook Packages: Springer Book Archive

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