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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/18623

Title: Using Functional Electrical Stimulation Mediated by Iterative Learning Control and Robotics to Improve Arm Movement for People With Multiple Sclerosis.
Authors: Sampson, P.
Freeman, C.
Coote, S.
Demain, S.
Feys, Peter
Meadmore, K.
Hughes, A.M.
Issue Date: 2015
Citation: IEEE transactions on neural systems and rehabilitation engineering, 24 (2), pag. 235-248
Abstract: Few interventions address multiple sclerosis (MS) arm dysfunction but robotics and functional electrical stimulation (FES) appear promising. This paper investigates the feasibility of combining FES with passive robotic support during virtual reality (VR) training tasks to improve upper limb function in people with multiple sclerosis (pwMS). The system assists patients in following a specified trajectory path, employing an advanced model-based paradigm termed iterative learning control (ILC) to adjust the FES to improve accuracy and maximise voluntary effort. Reaching tasks were repeated six times with ILC learning the optimum control action from previous attempts. A convenience sample of five pwMS was recruited from local MS societies, and the intervention comprised 18 one-hour training sessions over 10 weeks. The accuracy of tracking performance without FES and the amount of FES delivered during training were analysed using regression analysis. Clinical functioning of the arm was documented before and after treatment with standard tests. Statistically significant results following training included: improved accuracy of tracking performance both when assisted and unassisted by FES; reduction in maximum amount of FES needed to assist tracking; and less impairment in the proximal arm that was trained. The system was well tolerated by all participants with no increase in muscle fatigue reported. This study confirms the feasibility of FES combined with passive robot assistance as a potentially effective intervention to improve arm movement and control in pwMS and provides the basis for a follow up study.
Notes: Sampson, P; Demain, S; Hughes, AM (reprint author), Univ Southampton, Rehabil & Hlth Technol Res Grp, Fac Hlth Sci, Southampton SO9 5NH, Hants, England. P.Sampson@soton.ac.uk; cf@ecs.soton.ac.uk; Susan.Coote@ul.ie; S.H.Demain@soton.ac.uk; peter.feys@uhasselt.be; klm301@soton.ac.uk; A.Hughes@soton.ac.uk
URI: http://hdl.handle.net/1942/18623
DOI: 10.1109/TNSRE.2015.2413906
ISI #: 000372027300005
ISSN: 1534-4320
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

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