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Direct Estimation of Wrist Joint Angular Velocities from Surface EMGs by Using an SDNN Function Approximator

  • Kazumasa HorieEmail author
  • Atsuo Suemitsu
  • Tomohiro Tanno
  • Masahiko Morita
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9950)

Abstract

The present paper proposes a method for estimating joint angular velocities from multi-channel surface electromyogram (sEMG) signals. This method uses a selective desensitization neural network (SDNN) as a function approximator that learns the relation between integrated sEMG signals and instantaneous joint angular velocities. A comparison experiment with a Kalman filter model shows that this method can estimate wrist angular velocities in real time with high accuracy, especially during rapid motion.

Keywords

Surface electromyogram Angular velocity estimation Selective desensitization neural network 

Notes

Acknowledgment

This work was supported partly by JSPS KAKENHI grant numbers 22300079 and 24700593 and by Tateishi Science and Technology Foundation grant number 2157011.

References

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Kazumasa Horie
    • 1
    Email author
  • Atsuo Suemitsu
    • 2
  • Tomohiro Tanno
    • 1
  • Masahiko Morita
    • 1
  1. 1.University of TsukubaTsukubaJapan
  2. 2.Sapporo University of Health SciencesSapporoJapan

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