RISE Controller Tuning and System Identification Through Machine Learning for Human Lower Limb Rehabilitation via Neuromuscular Electrical Stimulation

Abstract

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.