Classification of Electromyographic Signals: Comparing Evolvable Hardware to Conventional Classifiers
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{ka-gl-gr-12a,
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author = "Paul Kaufmann and Kyrre Glette and Thiemo Gruber and
Marco Platzner and Jim Torresen and Bernhard Sick",
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title = "Classification of Electromyographic Signals: Comparing
Evolvable Hardware to Conventional Classifiers",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2013",
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volume = "17",
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number = "1",
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pages = "46--63",
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month = feb,
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keywords = "genetic algorithms, genetic programming, Cartesian
genetic programming, classification of
electromyographic signals, EHW, evolvable hardware,
functional unit row architecture, prosthetic hand
control",
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ISSN = "1089-778X",
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DOI = "doi:10.1109/TEVC.2012.2185845",
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size = "18 pages",
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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.",
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notes = "also known as \cite{6151104}",
- }
Genetic Programming entries for
Paul Kaufmann
Kyrre Harald Glette
Thiemo Gruber
Marco Platzner
Jim Torresen
Bernhard Sick
Citations