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

Post-Stroke Rehabilitation Monitoring Using Wireless Surface Electromyography: A Case Study

Well then write up a protocol on its use. Or are you that fucking lazy and incompetent that you won't actually help stroke survivors? This article is the best effort you can do? 

Post-Stroke Rehabilitation Monitoring Using Wireless Surface Electromyography: A Case Study

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Arvind C Ganesh ; B S Renganathan ; C Rajakumaran ; S P Preejith ; Khandelwal Shubham ; Joseph Jayaraj ; Sivaprakasam Mohanasankar

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Abstract:

Post-stroke rehabilitation monitoring provides key insights which can be used for development of customized treatment plans for patients. Rehabilitation monitoring systems available today are limited to observational measurements performed over a short period of time. Long term monitoring of stroke patients is necessary to keep track of stroke recovery and assess the patient’s response to the therapist’s treatment technique. This work is a case study that focuses on investigating the effects on muscle recruitment in bicep and calf muscles with and without orthotic intervention. A wireless surface Electromyography (sEMG) device is developed for monitoring muscle recruitment. Monitoring is done on a hemiplegic subject, before and after the physiotherapy treatment sessions, over duration of four months. An increase in sEMG peak frequency was observed after therapy in the absence of orthotic intervention while there was reduction in the peak frequency post therapy with orthotic intervention. Functional Independence Measurement scale, used to assess a patient’s level of disability as well as change in patient status in response to medical intervention is used as a reference measure to validate the sEMG device. The substantial changes in muscle recruitment due to regular therapy and orthotic intervention found in the study supports the use of the developed sEMG device as a surrogate to existing devices

Published in: 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

Date of Conference: 11-13 June 2018

Date Added to IEEE Xplore: 20 August 2018

ISBN Information:

DOI: 10.1109/MeMeA.2018.8438806

Publisher: IEEE

Conference Location: Rome, Italy, Italy 

I. Introduction

Worldwide, stroke is ranked as the leading cause of disability [1] . Due to stroke, coordination and muscle recruitment are commonly impaired. Up to 88% of people affected by stroke suffer hemiparesis with disorders of gait and balance, which persists even in the chronic phase [2] . Individuals suffering stroke actively overcome sensorimotor issues and asymmetry by using supportive or assistive equipments for increasing their gait ability. Quantifying normative patterns of muscle recruitment and coordination during common clinical tests can provide the neuromuscular demand required for common tasks and provide baselines for evaluating stroke patients. Currently, two main mechanisms contribute to stroke recovery. The first mechanism relates to functional recovery due to compensation based on improved use and refinement of remaining motor functions [3] , [4] . The second postulated mechanism assumes real recovery, i.e. restoration of lost brain functions due to learning-dependent reorganization of the brain [5] . Consequently, significant efforts are focused on gait retraining during rehabilitation following a stroke and efforts to develop and improve locomotor retraining programs are a major focus of rehabilitation research. Electromyographic (EMG) recordings provide a window into the central nervous system to evaluate muscle recruitment and coordination. After stroke, EMG recordings have been used to evaluate synergistic patterns of muscle activity, control assistive devices and guide biofeedback training. Despite improvements in measuring equipment since the discovery of EMG [6] , much of the research in this area is limited to observational measurements performed over short periods of time in laboratory settings. A study with a proper follow-up of patients over long periods could only help capture the temporal changes in muscle activity using EMG. Such long-term follow-up is not easy especially when dealing with subjects affected by stroke or hemiparesis. However, such long-term monitoring of sEMG can help observe sympathetic arousal patterns and previously missed out trends which could help in tracking the progress of patients. Monitoring a stroke patient can also uncover otherwise unperceived coping difficulties.
More at link. 

 

Spatial analysis of muscular activations in stroke survivors

I don't see how this helps us get to 100% recovery.
http://search.naric.com/research/rehab/redesign_record.cfm?search=2&type=all&criteria=O20799&phrase=no&rec=133942&article_source=Rehab&international=0&international_language=&international_location=
In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society , Pgs. 6058-6061.

NARIC Accession Number: O20799.  What's this?
ISSN: 1557-170X.
Author(s): Rasool, Ghulam; Afsharipour, Babak; Suresh, Nina L.; Hu, Xiaogang; Rymer, William Z..
Project Number: 90RE5013 (formerly H133E130019).
Publication Year: 2015.
Number of Pages: 4.
Abstract: Study investigated the spatial patterns of electrical activity in stroke-affected muscles using the high density surface electromyogram (sEMG) grids. sEMG signals were acquired from the impaired as well as contralateral biceps brachii muscles of stroke survivors and from healthy participants at various force levels from 20 to 60 percent of maximum voluntary contraction in an isometric non-fatiguing recording protocol. The spatial sEMG pattern was found to be consistent across force levels in healthy and stroke subjects. However, once compared across sides (left vs right in healthy and impaired vs. contralateral in stroke), the stroke-affected sides were found to be significantly different in distribution pattern of sEMG from the contralateral side. The sEMG activity areas were significantly shrunk on the affected sides indicating muscle atrophy due to stroke.
Descriptor Terms: ELECTROPHYSIOLOGY, MUSCULAR IMPAIRMENTS, STROKE.


Can this document be ordered through NARIC's document delivery service*?: Y.

Citation: Rasool, Ghulam, Afsharipour, Babak, Suresh, Nina L., Hu, Xiaogang, Rymer, William Z.. (2015). Spatial analysis of muscular activations in stroke survivors.  In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society , Pgs. 6058-6061. Retrieved 7/14/2017, from REHABDATA database.