Towards a robotic knee exoskeleton control based on human motion intention through EEG and sEMG signals

I looked but didn't find anything that suggested that my problem of knee snapping was addressed by any of these. I'm assuming that this is because my pre-motor cortex is mostly dead. My doctor explained absolutely nothing about why my deficits were occurring, he was completely useless.

 Towards a robotic knee exoskeleton control based on human motion intention through EEG and sEMG signals

 A.C.Villa-Parra a,b,

D.Delisle-Rodríguez a,c, 

A. López-Delis c, 

T. Bastos-Filho a,*,

R. Sagaró d, 

A. Frizera-Neto a
a Post-Graduate Program in Electrical Engineering, Universidade Federal do Espírito Santo, Vitória,Brazil
b GIIB, Universidad Politécnica Salesiana, Cuenca, Ecuador
c Center of Medical Biophysics, Universidad de Oriente, Santiago,Cuba
d  Mechanical and Design Engineering Department, Universidad de Oriente, Santiago,Cuba

Abstract

The integration of lower limb exoskeletons with robotic walkers allows obtaining a system to improve mobility and security duringgait rehabilitation. In this work, the evaluation of human motion intention (HMI) based on electroencephalogram (EEG) and surface electromyography (sEMG) signals are analyzed for a knee exoskeleton control as a preliminary study for gait neuro-rehabilitation with a hybrid robotic system. This system consists of the knee exoskeleton H2 and the UFES’s Smart Walker, which are used to restore the neuromotor control function of subjects with neural injuries. An experimental protocol was developed to identify patterns to control the exoskeleton in accordance with the HMI-based on EEG/sEMG. The EEG and sEMG signals are recorded during the following activities: stand-up/sit-down and knee flexion/extension. HMI is analyzed through  both event-related desynchronization/synchronization (ERD/ERS) and slow cortical potential, as well as the myoelectric  pattern classification related to lower limb. The feature extraction from sEMG signals is based on vector combinations in time and frequency domain which are used for a pattern classification stage trough an artificial neural network with Levenberg Marquadt training algorithm and support vector machine. Preliminary results shown that a combination of EEG/sEMG signals can be used to define a control strategy for the robotic system.©2015The Authors.Published by Elsevier