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

Neuromuscular electrical stimulation (NMES) has been effectively applied in many rehabilitation treatments of spinal cord injured (SCI) individuals. In this context, we introduce a novel robust and intelligent control-based methodology to closed-loop NMES systems. Our approach uses a control law to guarantee the system's stability. And, machine learning tools for both optimizing the controller parameters and system identification, with the novelty of using past rehabilitation data. In this paper, we apply the proposed methodology to the rehabilitation of lower limbs using a control technique namely robust integral of the sign of the error (RISE), an off-line improved genetic algorithm optimizer, and neural network models. Although in the literature the RISE controller presented good results on healthy subjects without any fine-tuning method, a trial and error approach would quickly lead to muscle fatigue for SCI individuals. Therefore, in this paper, for the first time, the RISE controller is evaluated with two paraplegic subjects in one stimulation session. And, with seven healthy individuals during at least one session up to at most five ones. As shown in results, control performance is improved via the proposed approach comparing to an empirical tuning, which can avoid premature fatigue on clinical procedures using NMES.Comment: 31 pages, 7 figures, 3 table