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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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