Detecting Localised Muscle Fatigue during Isometric Contraction using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Ahmed Kattan and Mohammed Al-Mulla and 
                 Francisco Sepulveda and Riccardo Poli",
  title =        "Detecting Localised Muscle Fatigue during Isometric
                 Contraction using Genetic Programming",
  year =         "2009",
  booktitle =    "International Conference on Evolutionary Computation
                 (ICEC 2009)",
  editor =       "Agostinho Rosa",
  pages =        "292--297",
  address =      "Madeira, Portugal",
  month =        "5-7 " # oct,
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-989-674-014-6",
  URL =          "",
  bibdate =      "2010-03-03",
  bibsource =    "DBLP,
  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.",
  notes =        "broken

Genetic Programming entries for Ahmed Kattan Mohammed Al-Mulla Francisco Sepulveda Riccardo Poli