Movement Identification in EMG Signals Using Machine Learning: A Comparative Study

  • Laura Lasso-ArciniegasEmail author
  • Andres Viveros-Melo
  • José A. Salazar-Castro
  • Miguel A. Becerra
  • Andrés Eduardo Castro-Ospina
  • E. Javier Revelo-Fuelagán
  • Diego H. Peluffo-Ordóñez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11047)


The analysis of electromyographic (EMG) signals enables the development of important technologies for industry and medical environments, due mainly to the design of EMG-based human-computer interfaces. There exists a wide range of applications encompassing: Wireless-computer controlling, rehabilitation, wheelchair guiding, and among others. The semantic interpretation of EMG analysis is typically conducted by machine learning algorithms, and mainly involves stages for signal characterization and classification. This work presents a methodology for comparing a set of state-of-the-art approaches of EMG signal characterization and classification within a movement identification framework. We compare the performance of three classifiers (KNN, Parzen-density-based classifier and ANN) using spectral (Wavelets) and time-domain-based (statistical and morphological descriptors) features. Also, a methodology for movement selection is proposed. Results are comparable with those reported in literature, reaching classification performance of (90.89 ± 1.12)% (KNN), (93.92 ± 0.34)% (ANN) and 91.09 ± 0.93 (Parzen-density-based classifier) with 12 movements.


ANN EMG signals Feature extraction KNN Parzen 



This work is supported by the “Smart Data Analysis Systems - SDAS” group (, as well as the “Grupo de Investigación en Ingeniería Eléctrica y Electrónica - GIIEE” from Universidad de Nariño. Also, the authors acknowledge to the research project supported by Agreement No. 095 November 20th, 2014 by VIPRI from Universidad de Nariño.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Laura Lasso-Arciniegas
    • 1
    Email author
  • Andres Viveros-Melo
    • 1
  • José A. Salazar-Castro
    • 1
    • 2
  • Miguel A. Becerra
    • 3
  • Andrés Eduardo Castro-Ospina
    • 3
  • E. Javier Revelo-Fuelagán
    • 1
  • Diego H. Peluffo-Ordóñez
    • 2
    • 4
  1. 1.Universidad de NariñoPastoColombia
  2. 2.Corporación Universitaria Autónoma de NariñoPastoColombia
  3. 3.Instituto Tecnológico Metropolitano (ITM)MedellínColombia
  4. 4.Yachay TechUrcuquiEcuador

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