Classification of Electromyographic Signals: Comparing Evolvable Hardware to Conventional Classifiers

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@Article{ka-gl-gr-12a,
  author =       "Paul Kaufmann and Kyrre Glette and Thiemo Gruber and 
                 Marco Platzner and Jim Torresen and Bernhard Sick",
  title =        "Classification of Electromyographic Signals: Comparing
                 Evolvable Hardware to Conventional Classifiers",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2013",
  volume =       "17",
  number =       "1",
  pages =        "46--63",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 genetic programming, classification of
                 electromyographic signals, EHW, evolvable hardware,
                 functional unit row architecture, prosthetic hand
                 control",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2012.2185845",
  size =         "18 pages",
  abstract =     "Evolvable hardware (EHW) has shown itself to be a
                 promising approach for prosthetic hand controllers.
                 Besides competitive classification performance, EHW
                 classifiers offer self-adaptation, fast training, and a
                 compact implementation. However, EHW classifiers have
                 not yet been sufficiently compared to state-of-the-art
                 conventional classifiers. In this paper, we compare two
                 EHW approaches to four conventional classification
                 techniques: k-nearest-neighbour, decision trees,
                 artificial neural networks, and support vector
                 machines. We provide all classifiers with features
                 extracted from electromyographic signals taken from
                 forearm muscle contractions, and let the algorithms
                 recognize eight to eleven different kinds of hand
                 movements. We investigate classification accuracy on a
                 fixed data set and stability of classification error
                 rates when new data is introduced. For this purpose, we
                 have recorded a short-term data set from three
                 individuals over three consecutive days and a long-term
                 data set from a single individual over three weeks.
                 Experimental results demonstrate that EHW approaches
                 are indeed able to compete with state-of-the-art
                 classifiers in terms of classification performance.",
  notes =        "also known as \cite{6151104}",
}

Genetic Programming entries for Paul Kaufmann Kyrre Harald Glette Thiemo Gruber Marco Platzner Jim Torresen Bernhard Sick

Citations