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dc.contributor.authorTyryshkin, Kathrinen
dc.date2014-09-29 17:06:09.217
dc.date.accessioned2014-09-30T00:11:02Z
dc.date.available2014-09-30T00:11:02Z
dc.date.issued2014-09-29
dc.identifier.urihttp://hdl.handle.net/1974/12514
dc.descriptionThesis (Ph.D, Computing) -- Queen's University, 2014-09-29 17:06:09.217en
dc.description.abstractStroke is an acute injury of the central nervous system and is caused by the disruption of blood flow or by the rupture of blood vessels. A stroke can impact many body functions, often causing motor, speech, memory, vision and other sensory impairments. It is highly prevalent and often requires long hospitalizations and post-stroke rehabilitation. The key to successful rehabilitation after stroke is an accurate assessment of the impairment. Current clinical assessments of stroke-related impairments involve physical assessment and visual observation by physicians. Existing clinical scores of upper limb function often use observer-based ordinal scales that are subjective and commonly have floor and ceiling effects. Therefore, these methods are not adequate to reliably discriminate different levels of performance. Robotics and integrated virtual reality systems have a tremendous potential to be used in computational systems that analyze, visualize and aid clinicians to identify and assess sensory-motor impairments. This thesis presents a framework for analysis and extraction of reliable and valid features from robotic data that can be used to accurately and objectively assess neurological impairments. The framework was applied on the Object Hit task that assesses the ability of participants to select and engage motor actions with both hands. In addition, the Object Hit Task was compared to the Object Hit and Avoid task, which is a slight modification of the original task. The comparison was done using a developed feature and task analysis framework. This framework encompasses similarities and differences between tasks in a given experiment in terms of feature information. The results showed that for the data used in the analysis, Object Hit task is able to identify impairments more effectively than the Object Hit and Avoid task. The overall results demonstrate that the Object Hit task provides an objective and easy approach to quantify upper limb motor function and visuospatial skills after stroke. The developed assessment tool can also be applied for diagnosis and prognosis of other neurological deficits, beyond stroke assessment.en
dc.language.isoengen
dc.relation.ispartofseriesCanadian thesesen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjecttask analysisen
dc.subjectdata miningen
dc.subjectfuzzy neural networken
dc.subjectfeature selectionen
dc.subjectstroke assessmenten
dc.subjecttask comparisonen
dc.subjectfeature informationen
dc.subjectrobotic technologyen
dc.titleAdvanced Techniques for Robotic Assessment of Neurological Impairments In Stroke Patientsen
dc.typethesisen
dc.description.degreePhDen
dc.contributor.supervisorGlasgow, Janice I.en
dc.contributor.supervisorScott, Stephen H.en
dc.contributor.departmentComputingen
dc.degree.grantorQueen's University at Kingstonen


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