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EMG-Based Detection of User’s Intentions for Human-Machine Shared Control of an Assistive Upper-Limb Exoskeleton

  • A. Accogli
  • L. Grazi
  • S. Crea
  • A. Panarese
  • J. Carpaneto
  • N. Vitiello
  • S. Micera
Conference paper
Part of the Biosystems & Biorobotics book series (BIOSYSROB, volume 16)

Abstract

To assist people with disabilities, exoskeletons must be provided with human-machine interfaces (HMI) capable to identify the user’s intentions and enable cooperative interaction. Electromyographic (EMG) signals could be suitable for this purpose, but their usability and effectiveness for shared control schemes in assistive devices is currently unclear. Here we developed advanced machine learning (ML) algorithms for detecting the user’s motion intention and decoding the intended movement direction, and discuss their applicability to the control of an upper-limb exoskeleton used as an assistive device for people with severe arm disabilities.

Keywords

Movement Direction Gaussian Mixture Model Assistive Device Movement Onset Motion Intention 
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.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • A. Accogli
    • 1
  • L. Grazi
    • 1
  • S. Crea
    • 1
  • A. Panarese
    • 1
  • J. Carpaneto
    • 1
  • N. Vitiello
    • 1
  • S. Micera
    • 1
    • 2
  1. 1.Biorobotics InstituteScuola Superiore Sant’AnnaPisaItaly
  2. 2.Center for Neuroprosthetics and Institute of BioengineeringEcole Polytechnique Federale de Lausanne (EPFL)LausanneSwitzerland

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