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dc.contributor.advisorHammond, Michaelen_US
dc.contributor.authorBaker, Todd Adam
dc.creatorBaker, Todd Adamen_US
dc.date.accessioned2011-12-05T22:00:47Z
dc.date.available2011-12-05T22:00:47Z
dc.date.issued2008en_US
dc.identifier.urihttp://hdl.handle.net/10150/193742
dc.description.abstractA biomechanical model of the human tongue was constructed, based upon a detailed anatomical study of an actual cadaver. Data from the Visible Human Project were segmented to create a volumetric representation of the tongue and its constituent muscles. The volumetric representation was converted to a smooth NURBS-bounded solid model--for compatibility with meshing algorithms--by lofting between splines, the vertices of which were defined by the coordinates of a smoothed triangular mesh representation. Using a hyperelastic constitutive model that allowed for the addition of active stress, the model deforms in response to user-specified muscle activation patterns. A series of meshes was created to perform a mesh validation study; in the validation tests performed, a 245,223-element mesh was found to be sufficient to model tongue behavior.Systematic samples of the behavior of the model were collected. Principal component analyses were performed on the samples to discover low-dimensional representations of tongue postures. Statistical models (linear regression models and neural networks) were fit to predict tongue posture from muscle activation, and vice versa. In all tests, it was found that a relatively small sample of tongue postures can be used to successfully generalize to larger data sets.Finally, a variety of specific tests were performed, based on claims and predictions found in previous literature. Of these, the claims of the muscular hydrostat theory of tongue movement were best supported. Simulations were also run that simulated lingual hemiplegia. It was found that substantially different muscular activation patterns were required to achieve equivalent postures in a hemiplegic tongue, relative to a normal tongue.
dc.language.isoENen_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.subjectbiomechanicalen_US
dc.subjectcomponent analysisen_US
dc.subjectfinite elementen_US
dc.subjecttongueen_US
dc.titleA biomechanical model of the human tongue for understanding speech production and other lingual behaviorsen_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.contributor.chairHammond, Michaelen_US
dc.identifier.oclc659750693en_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.contributor.committeememberHammond, Michaelen_US
dc.contributor.committeememberStory, Brad H.en_US
dc.contributor.committeememberVande Geest, Jonathan P.en_US
dc.identifier.proquest10137en_US
thesis.degree.disciplineLinguisticsen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.namePh.D.en_US
refterms.dateFOA2018-08-24T19:45:47Z
html.description.abstractA biomechanical model of the human tongue was constructed, based upon a detailed anatomical study of an actual cadaver. Data from the Visible Human Project were segmented to create a volumetric representation of the tongue and its constituent muscles. The volumetric representation was converted to a smooth NURBS-bounded solid model--for compatibility with meshing algorithms--by lofting between splines, the vertices of which were defined by the coordinates of a smoothed triangular mesh representation. Using a hyperelastic constitutive model that allowed for the addition of active stress, the model deforms in response to user-specified muscle activation patterns. A series of meshes was created to perform a mesh validation study; in the validation tests performed, a 245,223-element mesh was found to be sufficient to model tongue behavior.Systematic samples of the behavior of the model were collected. Principal component analyses were performed on the samples to discover low-dimensional representations of tongue postures. Statistical models (linear regression models and neural networks) were fit to predict tongue posture from muscle activation, and vice versa. In all tests, it was found that a relatively small sample of tongue postures can be used to successfully generalize to larger data sets.Finally, a variety of specific tests were performed, based on claims and predictions found in previous literature. Of these, the claims of the muscular hydrostat theory of tongue movement were best supported. Simulations were also run that simulated lingual hemiplegia. It was found that substantially different muscular activation patterns were required to achieve equivalent postures in a hemiplegic tongue, relative to a normal tongue.


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