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GMM-Based Single-Joint Angle Estimation Using EMG Signals

  • Stefano Michieletto
  • Luca Tonin
  • Mauro Antonello
  • Roberto Bortoletto
  • Fabiola Spolaor
  • Enrico Pagello
  • Emanuele Menegatti
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)

Abstract

This paper aims to explore the possibility to use Electromyography (EMG) to train a Gaussian Mixture Model (GMM) in order to estimate the bending angle of a single human joint. In particular, EMG signals from eight leg muscles and the knee joint angle are acquired during a kick task from three different subjects. GMM is validated on new unseen data and the classification performances are compared with respect to the number of EMG channels and the number of collected trials used during the training phase. Achieved results show that our framework is able to obtain high performances even using few EMG channels and with a small training dataset (Normalized Mean Square Error: 0.96, 0.98, 0.98 for the three subjects, respectively), opening new and interesting perspectives for the hybrid control of humanoid robots and exoskeletons.

Keywords

EMG signals Gaussian mixture model Gaussian mixture regression Single-joint angle estimation 

Notes

Acknowledgments

This research has been supported by “Consorzio Ethics” through a grant for research activity on the project “Rehabilitation Robotics”.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Stefano Michieletto
    • 1
  • Luca Tonin
    • 1
  • Mauro Antonello
    • 1
  • Roberto Bortoletto
    • 1
  • Fabiola Spolaor
    • 2
  • Enrico Pagello
    • 1
  • Emanuele Menegatti
    • 1
  1. 1.Intelligent Autonomous Systems Lab (IAS-Lab), Department of Information Engineering (DEI)University of PadovaPaduaItaly
  2. 2.Movement Analysis Laboratory, Department of Information Engineering (DEI)University of PadovaPaduaItaly

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