DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Machine Learning methods for EMG-based stroke patient movement analysis

Künemund, Maren (2014) Machine Learning methods for EMG-based stroke patient movement analysis. DLR-Interner Bericht. DLR-IB 572-2014-04. Master's. Technische Universität München. 45 S.

This is the latest version of this item.

[img] PDF


Stroke causes loss of brain functionality leading to restricted muscle activity on the affected body side. This directly impacts the Electromyography (EMG) signal. In order to develop a robotic rehabilitation system for stroke patients, the signal changes have to be analyzed. Hence, the goal of this master’s thesis is to identify the latent dimensionality of EMG data and to derive a criterion for the health status of stroke patients. Thus, an experiment has been conducted with seven stroke patients and two reference subjects. Different machine learning methods have been evaluated. At first, dimensionality reduction techniques c.f. PCA, NMF, and Gain Shape k-Means are utilized and evaluated using clustering scores. Those scores show, that reference subjects have higher values, whereas patients have values below a threshold. Applying PCA and NMF synergies have been identified. In order to preserve at least 95% of the information contained, data of patients need more synergies than data of reference subjects. Furthermore, patients’ patterns changes considerably on both sides compared to reference subjects’. These results show that synergies are very promising and insightful to evaluate EMG data. Further investigations are necessary to be able to generalize these findings.

Item URL in elib:https://elib.dlr.de/90268/
Document Type:Monograph (DLR-Interner Bericht, Master's)
Title:Machine Learning methods for EMG-based stroke patient movement analysis
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Künemund, Marenmaren.künemund (at) dlr.deUNSPECIFIED
Date:24 February 2014
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Number of Pages:45
Keywords:machine learning, stroke, EMG, signal analysis, dimensionality reduction, rehabilitation, latent dimensionality, synergies
Institution:Technische Universität München
Department:Fakultät für Informatik
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - On-Orbit Servicing [SY]
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Autonomy and Teleoperation
Deposited By: Hornung, Rachel
Deposited On:20 Oct 2014 11:13
Last Modified:31 Jul 2019 19:47

Available Versions of this Item

  • Machine Learning methods for EMG-based stroke patient movement analysis. (deposited 20 Oct 2014 11:13) [Currently Displayed]

Repository Staff Only: item control page

Help & Contact
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.