Classification of Electromyographic Signals: Comparing Evolvable Hardware to Conventional Classifiers

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 t...

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Vydané v:	IEEE transactions on evolutionary computation Ročník 17; číslo 1; s. 46 - 63
Hlavní autori:	Kaufmann, P., Glette, K., Gruber, T., Platzner, M., Torresen, J., Sick, B.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.02.2013 Institute of Electrical and Electronics Engineers
Predmet:	Applied sciences Biological and medical sciences Classification of electromyographic signals Computer science; control theory; systems Data processing. List processing. Character string processing Electrodiagnosis. Electric activity recording Electromyography Electronics embedded Cartesian genetic programming evolvable hardware Exact sciences and technology Feature extraction functional unit row architecture Hardware Information retrieval. Graph Integrated circuits Integrated circuits by function (including memories and processors) Investigative techniques, diagnostic techniques (general aspects) Medical sciences Memory organisation. Data processing Muscles Nervous system Pattern recognition prosthetic hand control Prosthetics Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices Signal processing algorithms Software Theoretical computing Short term Nearest neighbour evolvable hardware Data analysis Circuit design Prosthesis Evolutionary algorithm Adaptability Error rate Information rate Graph theory Neural network Decision tree Muscle contraction Classification of electromyographic signals prosthetic hand control Experimental result functional unit row architecture Genetic algorithm Vector support machine Electromyography Reconfigurable architectures embedded Cartesian genetic programming Pattern extraction
ISSN:	1089-778X, 1941-0026
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Popis
Shrnutí:	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-neighbor, 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.
ISSN:	1089-778X 1941-0026
DOI:	10.1109/TEVC.2012.2185845