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dc.contributor.advisor Farina, Dario Prof. Dr. Dr.
dc.contributor.author Vujaklija, Ivan
dc.date.accessioned 2017-09-06T10:31:53Z
dc.date.available 2017-09-06T10:31:53Z
dc.date.issued 2017-09-06
dc.identifier.uri http://hdl.handle.net/11858/00-1735-0000-0023-3EF1-F
dc.language.iso eng de
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.ddc 610 de
dc.title Translating Advanced Myocontrol for Upper Limb Prostheses from the Laboratory to Clinics de
dc.type doctoralThesis de
dc.contributor.referee Luke, Russell Prof. Dr.
dc.date.examination 2016-12-09
dc.description.abstracteng Versatility and dexterity in combination with supreme control makes human hands an unmatched tool for interacting with the world around us. Because of our dependence on hands, we are highly challenged in all aspects of our lives when their functionality is compromised. Considering the high incidence rate of partial or total hand function loss, development of suitable solutions for their substitution are of high priority. The devastating impact of a missing or a dysfunctional upper limb and the need for solutions to these impairments has been recognized centuries ago. Besides the obvious difficulties which it creates in performing everyday activities, it also reflects on the psychological and emotional state, with difficulties in social re-integration. This can lead to severe long-term consequences in everyday life. So far, commercial prosthetic limbs have failed to provide a solution capable of delivering intuitive and naturally looking control across several driveable joints. Regardless of being body powered or myoelectrically controlled, these systems depend on rather crude driving mechanisms, limiting the effectiveness of the provided solutions. These limitations eventually lead to rejection and abandonment of the technology. Academic research has addressed this challenge in various ways throughout the last 50 years, though a very limited number of solutions have reached the market. This fact indicates the size and the complexity of the problem of translating the laboratory based systems into the real world environment. The work presented in this thesis aims at addressing the aforementioned issues by enriching the amount of information that can be used for delivering control inputs over different prosthetic solutions. Theoretically, if the entire neural code sent from the brain to the muscles through the spinal cord could be decoded, its interpretation would allow natural and robust control over virtually any kind of prosthetic system. However, this requires the establishment of an interface that can access this detailed information. Here, several successful attempts to improve the control performance of prosthesis by advanced information methods of identification of the properties of the neural drive to muscles have been described and applied to already established prosthetic solutions. Focus has been put on translational potentials of these approaches and challenges which arise when systems initially developed in laboratory environment are further put to test in clinical setting. First, an in depth re-evaluation of the way in which the functional prosthetic assessment has been performed in academic and clinical studies is presented through a set of experiments. Comparison between the most commonly used offline evaluation technique and several typically applied clinical tests has been performed on a pool of transradial amputees. A poor correlation was found between the two sets of performance metrics, indicating the need of using more meaningful assessment scores in academic research to evaluate novel myoelectric systems. In addition, a kinematic analysis has been made during the execution of selected clinical tests, indicating that even the well-established clinical tools fail to completely evaluate all the aspects of the tested systems. The second set of experiments focused on advanced myoelectric control for patients who have sustained critical soft tissue injuries. So far, there has not been a suitable solution for recovering function in these severe cases. Here, through a combination of surgical interventions and rehabilitation technics, an interface for accessing myoelectric information sufficient for advanced control of sophisticated prosthetic technology has been established. A case series is presented to prove that surgical and engineering solutions can be combined for solving open clinical challenges through means of bionic reconstruction. The final set of experiments was designed to test the possibility of providing precise proportional control from motor unit spike trains originating in the spinal cord. The motor unit discharge patterns were decoded from high density surface EMG recordings obtained from reinnervated auxiliary muscles in the proximity of a high level amputation. This approach provides an enhanced prosthetic function across a difficult pool of transhumeral patients. Results presented here emphasise the importance of clinical testing of myoelectrical systems and provide an insight into the complexity of the translational challenges which arise once laboratory systems are exposed to the reality of clinical environment. The data provided in this thesis support the idea that advanced control approaches can be translated to effective clinical solutions even in cases that were earlier considered beyond the reach of myoelectric technologies. Finally, a new generation of neural interfaces, relying on the decoded neural drive to muscles, has been shown to be able to deliver highly refined control, and thus potentially revolutionize the way the prosthetic devices are driven. de
dc.contributor.coReferee Wendlandt, Robert Dr.
dc.contributor.thirdReferee Schilling, Arndt Prof. Dr
dc.contributor.thirdReferee Liebetanz, David Prof. Dr.
dc.contributor.thirdReferee Wörgötter, Florentin Prof. Dr.
dc.contributor.thirdReferee Fu, Xiaoming Prof. Dr.
dc.subject.eng Prosthetic control de
dc.subject.eng Human Machine Interface de
dc.subject.eng Myocontrol de
dc.subject.eng Upper limb de
dc.identifier.urn urn:nbn:de:gbv:7-11858/00-1735-0000-0023-3EF1-F-4
dc.affiliation.institute Medizinische Fakultät de
dc.subject.gokfull Medizin (PPN619874732) de
dc.identifier.ppn 1006090975 1000140040

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