Feasibility of Using Neuro-Fuzzy Subject-Specific Models for Functional Electrical Stimulation Induced Hand Movements

Eukene Imatz-Ojanguren, Eloy Irigoyen, David Valencia, Thierry Keller

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)
4 Downloads (Pure)

Abstract

Functional Electrical Stimulation (FES) is a technique that artificially elicits muscle contractions and it is used to restore motor/sensory functions in both assistive and therapeutic applications. The use of multi-field surface electrodes is a novel popular approach in transcutaneous FES applications. Lately, hybrid systems that combine artificial neural networks and fuzzy logic have also been proposed for many applications in different areas. This paper presents the possibility of combining both approaches for obtaining subject-specific models of FES induced hand movements for grasping applications. Data of the hand and finger motion from two subjects affected by acquired brain injury were used to train two different approaches: coactive neuro-fuzzy inference system and recurrent fuzzy neural network. Preliminary results show that these approaches can be considered in modelling applications for their ability to learn and predict main characteristics of the system, as well as providing useful information from the original system that could be interpreted as subject-specific knowledge.
Original languageEnglish
Pages (from-to)321-326
Number of pages6
Journalunknown
Volumeunknown
Issue number20
DOIs
Publication statusPublished - 1 Sept 2015
Event9th IFAC Symposium on Biological and Medical Systems, BMS 2015 - Berlin, Germany
Duration: 31 Aug 20152 Sept 2015

Keywords

  • Neuro-prosthetics
  • Functional Electrical Stimulation
  • Biological and medical system modelling
  • Fuzzy Neural Networks

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