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Echo State Networks as Novel Approach for Low-Cost Myoelectric Control

  • Cosima Prahm
  • Alexander Schulz
  • Benjamin Paaßen
  • Oskar Aszmann
  • Barbara Hammer
  • Georg Dorffner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10259)

Abstract

Myoelectric signals (EMG) provide an intuitive and rapid interface for controlling technical devices, in particular bionic arm prostheses. However, inferring the intended movement from a surface EMG recording is a non-trivial pattern recognition task, especially if the data stems from low-cost sensors. At the same time, overly complex models are prohibited by strict speed, data parsimony and robustness requirements. As a compromise between high accuracy and strict requirements we propose to apply Echo State Networks (ESNs), which extend standard linear regression with (1) a memory and (2) nonlinearity. Results show that both features, memory and nonlinearity, independently as well as in conjunction, improve the prediction accuracy on simultaneous movements in two degrees of freedom (hand opening/closing and pronation/supination) recorded from four able-bodied participants using a low-cost 8-electrode-array. However, it was also shown that the model is still not sufficiently resistant to external disturbances such as electrode shift.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Cosima Prahm
    • 1
  • Alexander Schulz
    • 2
  • Benjamin Paaßen
    • 2
  • Oskar Aszmann
    • 1
  • Barbara Hammer
    • 2
  • Georg Dorffner
    • 1
  1. 1.Medical University of ViennaViennaAustria
  2. 2.Bielefeld UniversityBielefeldGermany

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