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A Novel Robust and Intelligent Control Based Approach for Human Lower Limb Rehabilitation via Neuromuscular Electrical Stimulation

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Document pages: 31 pages

Abstract: Neuromuscular electrical stimulation (NMES) has been effectively applied inmany rehabilitation treatments of spinal cord injured (SCI) individuals. Inthis context, we introduce a novel robust and intelligent control-basedmethodology to closed-loop NMES systems. Our approach uses a control law toguarantee the system s stability. And, machine learning tools for bothoptimizing the controller parameters and system identification, with thenovelty of using past rehabilitation data. In this paper, we apply the proposedmethodology to the rehabilitation of lower limbs using a control techniquenamely robust integral of the sign of the error (RISE), an off-line improvedgenetic algorithm optimizer, and neural network models. Although in theliterature the RISE controller presented good results on healthy subjectswithout any fine-tuning method, a trial and error approach would quickly leadto muscle fatigue for SCI individuals. Therefore, in this paper, for the firsttime, the RISE controller is evaluated with two paraplegic subjects in onestimulation session. And, with seven healthy individuals during at least onesession up to at most five ones. As shown in results, control performance isimproved via the proposed approach comparing to an empirical tuning, which canavoid premature fatigue on clinical procedures using NMES.

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