  Neuromuscular electrical stimulation (NMES) has been effectively applied in
many rehabilitation treatments of individuals with spinal cord injury (SCI). In
this context, we introduce a novel, robust, and intelligent control-based
methodology to closed-loop NMES systems. Our approach utilizes a robust control
law to guarantee system stability and machine learning tools to optimize both
the controller parameters and system identification. Regarding the latter, we
introduce the use of past rehabilitation data to build more realistic
data-driven identified models. Furthermore, we apply the proposed methodology
for the rehabilitation of lower limbs using a control technique named the
robust integral of the sign of the error (RISE), an offline 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 individuals with SCI. 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 in at least two and at most five sessions.
The results showed that the proposed approach provided a better control
performance than empirical tuning, which can avoid premature fatigue on
NMES-based clinical procedures.
