Detecting Localised Muscle Fatigue during Isometric Contraction using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{conf/ijcci/KattanASP09,
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author = "Ahmed Kattan and Mohammed Al-Mulla and
Francisco Sepulveda and Riccardo Poli",
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title = "Detecting Localised Muscle Fatigue during Isometric
Contraction using Genetic Programming",
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year = "2009",
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booktitle = "International Conference on Evolutionary Computation
(ICEC 2009)",
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editor = "Agostinho Rosa",
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pages = "292--297",
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address = "Madeira, Portugal",
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month = "5-7 " # oct,
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-989-674-014-6",
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URL = "http://www.ahmedkattan.com/index_files/Camera_ready.pdf",
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bibdate = "2010-03-03",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/ijcci/ijcci2009.html#Salehi-AbariW09",
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abstract = "We propose the use of Genetic Programming (GP) to
generate new features to predict localised muscles
fatigue from pre-filtered surface EMG signals. In a
training phase, GP evolves programs with multiple
components. One component analyses statistical features
extracted from EMG to divide the signals into blocks.
The blocks' labels are decided based on the number of
zero crossings. These blocks are then projected onto a
two-dimensional Euclidean space via two further
(evolved) program components. K-means clustering is
applied to group similar data blocks. Each cluster is
then labeled into one of three types (Fatigue,
Transition-to-Fatigue and Non-Fatigue) according to the
dominant label among its members. Once a program is
evolved that achieves good classification, it can be
used on unseen signals without requiring any further
evolution. During normal operation the data are again
divided into blocks by the first component of the
program. The blocks are again projected onto a
two-dimensional Euclidean space by the two other
components of the program. Finally blocks are labelled
according to the k-nearest neighbours. The system
alerts the user of possible approaching fatigue once it
detects a Transition-to-Fatigue. In experimentation
with the proposed technique, the system provides very
encouraging results.",
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notes = "broken
http://www.icec.ijcci.org/Abstracts/2009/ICEC_2009_Abstracts.htm",
- }
Genetic Programming entries for
Ahmed Kattan
Mohammed Al-Mulla
Francisco Sepulveda
Riccardo Poli
Citations