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Using Myoelectric Signals to Classify Prehensile Patterns

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dc.contributor.advisorDuric, ZoranGerber, Lynn H.
dc.contributor.authorShuman, Gene R.
dc.creatorShuman, Gene R.
dc.date.accessioned2017-01-29T01:17:29Z
dc.date.available2017-01-29T01:17:29Z
dc.date.issued2016
dc.description.abstractPeople want to live independently, but too often disabilities or advanced age robs them of the ability to do the necessary activities of daily living (ADLs). Finding relationships between electromyograms measured in the arm and movements of the hand and wrist needed to perform ADLs can help address performance deficits and be exploited in designing myoelectrical control systems for prosthetics and computer interfaces.
dc.format.extent114 pages
dc.identifier.urihttps://hdl.handle.net/1920/10626
dc.identifier.urihttps://doi.org/10.13021/MARS/4560
dc.language.isoen
dc.rightsCopyright 2016 Gene R. Shuman
dc.subjectComputer science
dc.subjectBiomedical engineering
dc.subjectArtificial intelligence
dc.subjectActivities of daily living
dc.subjectBiomechanics
dc.subjectClassification
dc.subjectElectromyogram
dc.subjectPattern recognition
dc.subjectPrehensile pattern
dc.titleUsing Myoelectric Signals to Classify Prehensile Patterns
dc.typeDissertation
thesis.degree.disciplineComputer Science
thesis.degree.grantorGeorge Mason University
thesis.degree.levelPh.D.

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