Supervised and dynamic neuro-fuzzy systems to classify physiological responses in robot-assisted neurorehabilitation.

Journal: PloS one
Published Date: May 22, 2015

Abstract

This paper presents the application of an Adaptive Resonance Theory (ART) based on neural networks combined with Fuzzy Logic systems to classify physiological reactions of subjects performing robot-assisted rehabilitation therapies. First, the theoretical background of a neuro-fuzzy classifier called S-dFasArt is presented. Then, the methodology and experimental protocols to perform a robot-assisted neurorehabilitation task are described. Our results show that the combination of the dynamic nature of S-dFasArt classifier with a supervisory module are very robust and suggest that this methodology could be very useful to take into account emotional states in robot-assisted environments and help to enhance and better understand human-robot interactions.

Authors

  • Luis D Lledó
    Biomedical Neuroengineering Group, Universidad Miguel Hernández, Elche, Alicante, Spain.
  • Francisco J Badesa
    Biomedical Neuroengineering Group, Universidad Miguel Hernández, Elche, Alicante, Spain.
  • Miguel Almonacid
    Systems Engineering and Automation Department, Universidad Politécnica de Cartagena, Cartagena, Murcia, Spain.
  • José M Cano-Izquierdo
    Systems Engineering and Automation Department, Universidad Politécnica de Cartagena, Cartagena, Murcia, Spain.
  • José M Sabater-Navarro
    Biomedical Neuroengineering Group, Universidad Miguel Hernández, Elche, Alicante, Spain.
  • Eduardo Fernandez
    Neuroprosthetics and Visual Rehabilitation Research Unit, Bioengineering Institute, Miguel Hernández University, Alicante, Spain.
  • Nicolás Garcia-Aracil
    Biomedical Neuroengineering Group, Universidad Miguel Hernández, Elche, Alicante, Spain.

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