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Machine Learning methods for EMG-based stroke patient movement analysis

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

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Abstract

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
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Künemund, Marenmaren.künemund (at) dlr.deUNSPECIFIED
Date:24 February 2014
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Number of Pages:45
Status:Published
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 Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Technik für Raumfahrtsysteme
DLR - Research theme (Project):R - Vorhaben on Orbit Servicing
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

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