Supervised and dynamic neuro-fuzzy systems to classify physiological responses in robot-assisted neurorehabilitation.
Where this comes from
- Record sourced from PubMed, PMID 26001214.
- Also identified by DOI 10.1371/journal.pone.0127777 and PMC identifier 4441369.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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.
Medical subject headings
- Fuzzy Logic
- Neurological Rehabilitation
- Robotics