Comparing Evolvable Hardware to Conventional Classifiers for Electromyographic Prosthetic Hand Control

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Kyrre Glette and Thiemo Gruber and Paul Kaufmann and 
                 Jim Torresen and Bernhard Sick and Marco Platzner",
  title =        "Comparing Evolvable Hardware to Conventional
                 Classifiers for Electromyographic Prosthetic Hand
  booktitle =    "2008 NASA/ESA Conference on Adaptive Hardware and
  year =         "2008",
  pages =        "32--39",
  month =        "22-25 " # jun,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 Genetic Programming, ECGP, prosthetic hand control,
                 evolvable hardware, EHW, kNN, decision trees, DT,
                 support vector machines, SVM",
  isbn13 =       "978-0-7695-3166-3",
  DOI =          "doi:10.1109/AHS.2008.12",
  size =         "8 pages",
  abstract =     "Evolvable hardware has shown to be a promising
                 approach for prosthetic hand controllers as it features
                 self-adaptation, fast training, and a compact
                 system-on-chip implementation. Besides these intriguing
                 features, the classification performance is paramount
                 to success for any classifier. However, evolvable
                 hardware classifiers have not yet been sufficiently
                 compared to state-of-the-art conventional classifiers.
                 In this paper, we compare two evolvable hardware
                 approaches for signal classification to three
                 conventional classification techniques:
                 k-nearest-neighbour, decision trees, and support vector
                 machines. We provide all classifiers with features
                 extracted from electromyographic signals taken from
                 forearm muscle contractions, and try to recognise eight
                 different hand movements. Experimental results
                 demonstrate that evolvable hardware approaches are
                 indeed able to compete with state-of-the-art
                 classifiers. Specifically, one of our evolvable
                 hardware approaches delivers a generalisation
                 performance similar to that of support vector
  notes =        "also known as \cite{4584252}",

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