Towards a Robotic Knee Exoskeleton Control Based on Human Motion Intention through EEG and sEMGsignals

A. C. Villa-Parra, D. Delisle-Rodríguez, A. López-Delis, T. Bastos-Filho, R. Sagaró, A. Frizera-Neto

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

© 2015 The Authors The integration of lower limb exoskeletons with robotic walkers allows obtaining a system to improve mobility and security during gait 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.
Original languageEnglish (US)
Pages (from-to)1379-1386
Number of pages8
JournalProcedia Manufacturing
DOIs
StatePublished - 1 Jan 2015

Fingerprint

Dive into the research topics of 'Towards a Robotic Knee Exoskeleton Control Based on Human Motion Intention through EEG and sEMGsignals'. Together they form a unique fingerprint.

Cite this