TY - JOUR
T1 - Feasibility of Using Neuro-Fuzzy Subject-Specific Models for Functional Electrical Stimulation Induced Hand Movements
AU - Imatz-Ojanguren, Eukene
AU - Irigoyen, Eloy
AU - Valencia, David
AU - Keller, Thierry
N1 - Publisher Copyright:
© 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
PY - 2015/9/1
Y1 - 2015/9/1
N2 - 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.
AB - 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.
KW - Neuro-prosthetics
KW - Functional Electrical Stimulation
KW - Biological and medical system modelling
KW - Fuzzy Neural Networks
KW - Neuro-prosthetics
KW - Functional Electrical Stimulation
KW - Biological and medical system modelling
KW - Fuzzy Neural Networks
UR - http://www.scopus.com/inward/record.url?scp=84992512233&partnerID=8YFLogxK
U2 - 10.1016/j.ifacol.2015.10.159
DO - 10.1016/j.ifacol.2015.10.159
M3 - Conference article
VL - unknown
SP - 321
EP - 326
JO - unknown
JF - unknown
IS - 20
T2 - 9th IFAC Symposium on Biological and Medical Systems, BMS 2015
Y2 - 31 August 2015 through 2 September 2015
ER -