Probabilistic Control: Implications For The Development Of Upper Limb Neuroprosthetics

Persistent Link:
http://hdl.handle.net/10150/195648
Title:
Probabilistic Control: Implications For The Development Of Upper Limb Neuroprosthetics
Author:
Anderson, Chad
Issue Date:
2007
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Functional electrical stimulation (FES) involves artificial activation of paralyzed muscles via implanted electrodes. FES has been successfully used to improve the ability of tetraplegics to perform upper limb movements important for daily activities. The variety of movements that can be generated by FES is, however, limited to a few movements such as hand grasp and release. Ideally, a user of an FES system would have effortless command over all of the degrees of freedom associated with upper limb movement. One reason that a broader range of movements has not been implemented is because of the substantial challenge associated with identifying the patterns of muscle stimulation needed to elicit additional movements. The first part of this dissertation addresses this challenge by using a probabilistic algorithm to estimate the patterns of muscle activity associated with a wide range of upper limb movements.A neuroprosthetic involves the control of an external device via brain activity. Neuroprosthetics have been successfully used to improve the ability of tetraplegics to perform tasks important for interfacing with the world around them. The variety of mechanisms which they can control is, however, limited to a few devices such as special computer typing programs. Because motor areas of the cerebral cortex are known to represent and regulate voluntary arm movements it might be possible to sense this activity with electrodes and decipher this information in terms of a moment-by-moment representation of arm trajectory. Indeed, several methods for decoding neural activity have been described, but these approaches are encumbered by technical difficulties. The second part of this dissertation addresses this challenge by using similar probabilistic methods to extract arm trajectory information from electroencephalography (EEG) electrodes that are already chronically deployed and widely used in human subjects.Ultimately, the two approaches developed as part of this dissertation might serve as a flexible controller for interfacing brain activity with functional electrical stimulation systems to realize a brain-controlled upper-limb neuroprosthetic system capable of eliciting natural movements. Such a system would effectively bypass the injured region of the spinal cord and reanimate the arm, greatly increasing movement capability and independence in paralyzed individuals.
Type:
text; Electronic Dissertation
Keywords:
EMG estimation; arm trajectory; probabilistic control; bayesian inference
Degree Name:
PhD
Degree Level:
doctoral
Degree Program:
Electrical & Computer Engineering; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Fuglevand, Andrew J.; Higgins, Charles M.
Committee Chair:
Fuglevand, Andrew J.; Higgins, Charles M.

Full metadata record

DC FieldValue Language
dc.language.isoENen_US
dc.titleProbabilistic Control: Implications For The Development Of Upper Limb Neuroprostheticsen_US
dc.creatorAnderson, Chaden_US
dc.contributor.authorAnderson, Chaden_US
dc.date.issued2007en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractFunctional electrical stimulation (FES) involves artificial activation of paralyzed muscles via implanted electrodes. FES has been successfully used to improve the ability of tetraplegics to perform upper limb movements important for daily activities. The variety of movements that can be generated by FES is, however, limited to a few movements such as hand grasp and release. Ideally, a user of an FES system would have effortless command over all of the degrees of freedom associated with upper limb movement. One reason that a broader range of movements has not been implemented is because of the substantial challenge associated with identifying the patterns of muscle stimulation needed to elicit additional movements. The first part of this dissertation addresses this challenge by using a probabilistic algorithm to estimate the patterns of muscle activity associated with a wide range of upper limb movements.A neuroprosthetic involves the control of an external device via brain activity. Neuroprosthetics have been successfully used to improve the ability of tetraplegics to perform tasks important for interfacing with the world around them. The variety of mechanisms which they can control is, however, limited to a few devices such as special computer typing programs. Because motor areas of the cerebral cortex are known to represent and regulate voluntary arm movements it might be possible to sense this activity with electrodes and decipher this information in terms of a moment-by-moment representation of arm trajectory. Indeed, several methods for decoding neural activity have been described, but these approaches are encumbered by technical difficulties. The second part of this dissertation addresses this challenge by using similar probabilistic methods to extract arm trajectory information from electroencephalography (EEG) electrodes that are already chronically deployed and widely used in human subjects.Ultimately, the two approaches developed as part of this dissertation might serve as a flexible controller for interfacing brain activity with functional electrical stimulation systems to realize a brain-controlled upper-limb neuroprosthetic system capable of eliciting natural movements. Such a system would effectively bypass the injured region of the spinal cord and reanimate the arm, greatly increasing movement capability and independence in paralyzed individuals.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectEMG estimationen_US
dc.subjectarm trajectoryen_US
dc.subjectprobabilistic controlen_US
dc.subjectbayesian inferenceen_US
thesis.degree.namePhDen_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineElectrical & Computer Engineeringen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorFuglevand, Andrew J.en_US
dc.contributor.advisorHiggins, Charles M.en_US
dc.contributor.chairFuglevand, Andrew J.en_US
dc.contributor.chairHiggins, Charles M.en_US
dc.contributor.committeememberHiggins, Charles M.en_US
dc.contributor.committeememberBarton, Jennifer K.en_US
dc.contributor.committeememberMarcellin, Michael W.en_US
dc.identifier.proquest2283en_US
dc.identifier.oclc659748131en_US
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