Classification of localized muscle fatigue with genetic programming on sEMG during isometric contraction

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Al-Mulla:2009:EMBC,
  author =       "M. R. Al-Mulla and F. Sepulveda and M. Colley and 
                 A. Kattan",
  title =        "Classification of localized muscle fatigue with
                 genetic programming on sEMG during isometric
                 contraction",
  booktitle =    "Annual International Conference of the IEEE
                 Engineering in Medicine and Biology Society, EMBC
                 2009",
  year =         "2009",
  month =        "2-6 " # sep,
  address =      "Minneapolis, Minnesota, USA",
  pages =        "2633--2638",
  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",
  DOI =          "doi:10.1109/IEMBS.2009.5335368",
  ISSN =         "1557-170X",
  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.",
  notes =        "Also known as \cite{5335368}",
}

Genetic Programming entries for Mohammad R Al-Mulla Francisco Sepulveda Martin Colley Ahmed Kattan

Citations