Classification of localized muscle fatigue with genetic programming on sEMG during isometric contraction
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Al-Mulla:2009:EMBC,
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author = "M. R. Al-Mulla and F. Sepulveda and M. Colley and
A. Kattan",
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title = "Classification of localized muscle fatigue with
genetic programming on sEMG during isometric
contraction",
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booktitle = "Annual International Conference of the IEEE
Engineering in Medicine and Biology Society, EMBC
2009",
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year = "2009",
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month = "2-6 " # sep,
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address = "Minneapolis, Minnesota, USA",
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pages = "2633--2638",
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keywords = "genetic algorithms, genetic programming, GP training
phase, K-means clustering, fuzzy classifier, isometric
contraction, isometric sEMG signal filtering, localized
muscle fatigue classification, nonfatigue classifier,
rectified surface electromyography, statistical feature
extraction, transition-to-fatigue classifier,
two-dimensional Euclidean space, biomechanics,
electromyography, fatigue, feature extraction,
filtering theory, fuzzy logic, medical signal
processing, neurophysiology, pattern clustering, signal
classification, statistical analysis",
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DOI = "doi:10.1109/IEMBS.2009.5335368",
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ISSN = "1557-170X",
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abstract = "Genetic programming is used to generate a solution
that can classify localized muscle fatigue from
filtered and rectified surface electromyography (sEMG).
The GP has two classification phases, the GP training
phase and a GP testing phase. In the training phase,
the program evolved with multiple components. One
component analyzes statistical features extracted from
sEMG to chop the signal into blocks and label them
using a fuzzy classifier into three classes:
non-fatigue, transition-to-fatigue and fatigue. The
blocks are then projected onto a two-dimensional
Euclidean space via two further (evolved) program
components. K-means clustering is then applied to group
similar data blocks. Each cluster is then labeled
according to its dominant members. The programs that
achieve good classification are evolved. In the testing
phase, it tests the signal using the evolved
components, however without the use of a fuzzy
classifier. As the results show the evolved program
achieves good classification and it can be used on any
unseen isometric sEMG signals to classify fatigue
without requiring any further evolution. The GP was
able to classify the signal into a meaningful sequence
of non-fatigue -> transition-to-fatiguer -> fatigue. By
identifying a transition-to fatigue state the GP can
give a prediction of an oncoming fatigue. The genetic
classifier gave promising results 83.17percent correct
classification on average of all signals in the test
set, especially considering that the GP is classifying
muscle fatigue for ten different individuals.",
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notes = "Also known as \cite{5335368}",
- }
Genetic Programming entries for
Mohammad R Al-Mulla
Francisco Sepulveda
Martin Colley
Ahmed Kattan
Citations