| dc.contributor.advisor | Farina, Dario Prof. Dr. | |
| dc.contributor.author | Kapelner, Tamás | |
| dc.date.accessioned | 2016-12-16T09:03:54Z | |
| dc.date.available | 2017-12-01T23:50:08Z | |
| dc.date.issued | 2016-12-16 | |
| dc.identifier.uri | http://hdl.handle.net/11858/00-1735-0000-002B-7CE1-9 | |
| dc.identifier.uri | http://dx.doi.org/10.53846/goediss-6007 | |
| dc.language.iso | eng | de |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject.ddc | 610 | de |
| dc.title | Decoding motor neuron behavior for advanced control of upper limb prostheses | de |
| dc.type | doctoralThesis | de |
| dc.contributor.referee | Farina, Dario Prof. Dr. | |
| dc.date.examination | 2016-12-01 | |
| dc.description.abstracteng | One of the main challenges in upper limb prosthesis control to date is to provide devices
intuitive to use and capable to reproduce the natural movements of the arm and hand. One
approach to solve this challenge is to use the same control signals for prosthesis control
that our nervous system uses to control its muscles. This thesis aims to investigate the
possibility of natural, intuitive prosthesis control using neural information obtained with
available surface EMG decomposition methods. In order to explore all aspects of such a
novel approach, a series of five studies were performed with the final goal of implementing
a proof of concept and comparing its performance with state of the art myoelectric control.
The performed investigations revealed important insights in motor unit physiology after
targeted muscle reinnervation, EMG decomposition in dynamic voluntary contractions
of the forearm, and the properties and challenges of neural information based prosthesis
control. The main outcome of the thesis is that neural information based prosthesis
control is capable to outperform myoelectric approaches in pattern recognition, linear
regression and nonlinear regression, as determined by offline performance comparisons.
The final proof of concept for this novel approach was a robust regression method based
on neuromusculoskeletal modeling. The kinematics estimation of the proposed approach
outperformed EMG-based nonlinear regression in both able-bodied subjects and patients
with limb deficiency, indicating that using neural information is a promising avenue for
advanced myoelectric control. | de |
| dc.contributor.coReferee | Liebetanz, David PD Dr. | |
| dc.subject.eng | EMG | de |
| dc.subject.eng | Prosthesis control | de |
| dc.subject.eng | Neural information | de |
| dc.subject.eng | EMG decomposition | de |
| dc.subject.eng | TMR | de |
| dc.identifier.urn | urn:nbn:de:gbv:7-11858/00-1735-0000-002B-7CE1-9-4 | |
| dc.affiliation.institute | Medizinische Fakultät | de |
| dc.subject.gokfull | Medizin (PPN619874732) | de |
| dc.subject.gokfull | Medizinische Informatik (PPN619875089) | de |
| dc.subject.gokfull | Physik / Biopyhsik / Biomedizinische Technik - Allgemein- und Gesamtdarstellungen (PPN619875100) | de |
| dc.description.embargoed | 2017-11-31 | |
| dc.identifier.ppn | 875067603 | |