Combining two open source tools for neural computation (BioPatRec and Netlab) improves movement classification for prosthetic control.

Journal: BMC research notes
Published Date: Aug 31, 2016

Abstract

BACKGROUND: Controlling a myoelectric prosthesis for upper limbs is increasingly challenging for the user as more electrodes and joints become available. Motion classification based on pattern recognition with a multi-electrode array allows multiple joints to be controlled simultaneously. Previous pattern recognition studies are difficult to compare, because individual research groups use their own data sets. To resolve this shortcoming and to facilitate comparisons, open access data sets were analysed using components of BioPatRec and Netlab pattern recognition models.

Authors

  • Cosima Prahm
    Institute of Electrodynamics, Microwave and Circuit Engineering, Vienna University of Technology, Gusshausstr. 25, 1040, Vienna, Austria. e0509285@student.tuwien.ac.at.
  • Korbinian Eckstein
    Department of Radiology and Nuclear Medicine, Medical University of Vienna, Lazarettgasse 14, 1090, Vienna, Austria.
  • Max Ortiz-Catalan
    Division for Biomedical Engineering, Department of Signals and Systems, Chalmers University of Technology, Campus Johanneberg, 41296, Gothenburg, Sweden.
  • Georg Dorffner
    Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria.
  • Eugenijus Kaniusas
    Institute of Electrodynamics, Microwave and Circuit Engineering, Vienna University of Technology, Gusshausstr. 25, 1040, Vienna, Austria.
  • Oskar C Aszmann
    CD Laboratory for Extremity Reconstruction, Division of Plastic and Reconstructive Surgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.

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