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

Journal ar
  • Volumen: 10
  • Número: 5
  • Fecha: 22 mayo 2015
  • ISSN: 19326203
  • Tipo de fuente: Revista
  • DOI: 10.1371/journal.pone.0127777
  • Tipo de documento: Artículo
  • Editorial: Public Library of
© 2015 Lledó et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.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.

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