A Comparison of Myoelectric Pattern Recognition Methods to Control an Upper Limb Active Exoskeleton

  • Alberto López-Delis
  • Andrés Felipe Ruiz-Olaya
  • Teodiano Freire-Bastos
  • Denis Delisle-Rodríguez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)


Physically impaired people may use Surface Electromyography (sEMG) signals to control assistive devices in an automatic way. sEMG signals directly reflect the human motion intention, they can be used as input information for active exoskeleton control. This paper proposes a set of myoelectric algorithms based on machine learning for detecting movement intention aimed at controlling an upper limb active exoskeleton. The algorithms use a feature extraction stage based on a combination of time and frequency domain features (mean absolute value – waveform length, and auto-regressive model, respectively). The pattern recognition stage uses Linear Discriminant Analysis, K-Nearest Neighbor, Support Vector Machine and Bayesian classifiers. Additionally, two post-processing techniques are incorporated: majority vote and transition removal. The performance of the algorithms is evaluated with parameters of sensitivity, specificity, positive predictive value, error rate and active error rate, under typical conditions. These evaluations allow identifying pattern recognition algorithms for real-time control of an active exoskeleton.


Movement intention detection myoelectric patterns recognition machine learning majority vote surface electromyography transition removal 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alberto López-Delis
    • 1
  • Andrés Felipe Ruiz-Olaya
    • 2
  • Teodiano Freire-Bastos
    • 3
  • Denis Delisle-Rodríguez
    • 1
  1. 1.Medical Biophysics CenterUniversity of OrienteSantiago de CubaCuba
  2. 2.Faculty of Electronic and Biomedical EngineeringUniversity Antonio NariñoColombia
  3. 3.PPGEEFederal University of Espirito SantoVitoriaBrazil

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