A novel robust and intelligent control based approach for human lower limb rehabilitation via neuromuscular electrical stimulation

• Autor(a) principal: Arcolezi, Héber Hwang
• Data de Publicação: 2019
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://hdl.handle.net/11449/190755
• topic: Neuromuscular electrical stimulation; Knee joint control; RISE controller; Improved genetic algorithm; Recurrent neural networks; Estimulação elétrica neuromuscular; Controle da articulação do joelho; Controlador RISE; Algoritmo genético melhorado; Redes neurais recorrentes

In the last few years, several studies have been carried out showing that neuromuscular electrical stimulation (NMES) can produce good therapeutic results in patients with spinal cord injury (SCI). This research introduces a new robust and intelligent control-based methodology for human lower limb rehabilitation via NMES using a continuous-time control technique named robust integral of the sign of the error (RISE). Although in the literature the RISE controller has shown good results without any fine-tuning method, a trial and error approach would quickly lead to muscle fatigue in SCI patients. Therefore, it was shown in this study that the control performance for robustly tracking a reference signal can be improved through the proposed approach by providing an intelligent tuning for each voluntary. Simulation results with a mathematical model and eight identified subjects from the literature are provided, and real experiments are performed with seven healthy and two paraplegic subjects. Besides, this research introduces the application of deep and dynamic neural networks namely the multilayer perceptron, a simple recurrent neural network, and the Long Short-Term memory architecture, to identify the nonlinear and time-varying relationship between the supplied NMES and achieved angular position. Identification results indicate good fitting to data and very low mean square error using few data for training, proving to be very prospective methods for proposing control-oriented models.