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Public Library of Science, PLoS ONE, 5(10), p. e0127777, 2015

DOI: 10.1371/journal.pone.0127777

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Supervised and Dynamic Neuro-Fuzzy Systems to Classify Physiological Responses in Robot-Assisted Neurorehabilitation

Journal article published in 2015 by Luis D. Lledó, Francisco J. Badesa ORCID, Miguel Almonacid, José M. Cano-Izquierdo, José M. Sabater-Navarro, Eduardo Fernández ORCID, Nicolás Garcia-Aracil
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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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.