Investigating Evolvable Hardware Classification for the BioSleeve Electromyographic Interface

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

@InProceedings{gl-ka-13a,
  author =       "Kyrre Glette and Paul Kaufmann and 
                 Christopher Assad and Michael T. Wolf",
  title =        "Investigating Evolvable Hardware Classification for
                 the {BioSleeve} Electromyographic Interface",
  booktitle =    "International Conference on Evolvable Systems (ICES
                 2013)",
  year =         "2013",
  pages =        "73--80",
  month =        "27-31 " # jul,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, EHW",
  DOI =          "doi:10.1109/ICES.2013.6613285",
  size =         "8 pages",
  abstract =     "We investigate the applicability of an evolvable
                 hardware classifier architecture for electromyography
                 (EMG) data from the BioSleeve wearable human-machine
                 interface, with the goal of having embedded training
                 and classification. We investigate classification
                 accuracy for datasets with 17 and 11 gestures and
                 compare to results of Support Vector Machines (SVM) and
                 Random Forest classifiers. Classification accuracies
                 are 91.5percent for 17 gestures and 94.4percent for 11
                 gestures. Initial results for a field programmable
                 array (FPGA) implementation of the classifier
                 architecture are reported, showing that the classifier
                 architecture fits in a Xilinx XC6SLX45 FPGA. We also
                 investigate a bagging-inspired approach for training
                 the individual components of the classifier with a
                 subset of the full training data. While showing some
                 improvement in classification accuracy, it also proves
                 useful for reducing the number of training instances
                 and thus reducing the training time for the
                 classifier.",
  notes =        "JPL BioSleeve prototype, FUR, 1 + 4 evolution
                 strategy, NSGA-II, SVM, Weka Random Forests,
                 RapidMiner, VHDL FPGA Xilinx Spartan-6 XC6SLX45

                 Also known as \cite{6613285}",
}

Genetic Programming entries for Kyrre Harald Glette Paul Kaufmann Christopher Assad Michael T Wolf

Citations