Surface EMG based handgrip force predictions using gene expression programming

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@Article{Yang:2016:Neurocomputing,
  author =       "Zhongliang Yang and Yumiao Chen and Zhichuan Tang and 
                 Jianping Wang",
  title =        "Surface {EMG} based handgrip force predictions using
                 gene expression programming",
  journal =      "Neurocomputing",
  year =         "2016",
  ISSN =         "0925-2312",
  DOI =          "doi:10.1016/j.neucom.2016.05.038",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0925231216303903",
  abstract =     "The main objective of this study is to precisely
                 predict muscle forces from surface electromyography
                 (sEMG) for hand gesture recognition. A robust variant
                 of genetic programming, namely Gene Expression
                 Programming (GEP), is used to derive a new empirical
                 model of handgrip sEMG-force relationship. A series of
                 handgrip forces and corresponding sEMG signals were
                 recorded from 6 healthy male subjects and during 4
                 levels of percentage of maximum voluntary contraction
                 (percentMVC) in experiments. Using one-way ANOVA with
                 multiple comparisons test, 10 features of the sEMG time
                 domain were extracted from homogeneous subsets and used
                 as input vectors. Subsequently, a handgrip force
                 prediction model was developed based on GEP. In order
                 to compare the performance of this model, other models
                 based on a back propagation neural network and a
                 support vector machine were trained using the same
                 input vectors and data sets. The root mean square error
                 and the correlation coefficient between the actual and
                 predicted forces were calculated to assess the
                 performance of the three models . The results show that
                 the GEP model provide the highest accuracy and
                 generalization capability among the studied models. It
                 was concluded that the proposed GEP model is relatively
                 short, simple and excellent for predicting handgrip
                 forces based on sEMG signals.",
  keywords =     "genetic algorithms, genetic programming, Gene
                 expression programming, Surface electromyography, Grip
                 force, Force prediction",
}

Genetic Programming entries for Zhongliang Yang Yumiao Chen Zhichuan Tang Jianping Wang

Citations