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postgraduate thesis: Quantitative assessment of hand function by hand motion analysis usingcyberglove

TitleQuantitative assessment of hand function by hand motion analysis usingcyberglove
Authors
Advisors
Issue Date2012
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
Citation
Au, K. T. [區建熙]. (2012). Quantitative assessment of hand function by hand motion analysis using cyberglove. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b4784998
AbstractHand motion analysis methods have been providing researchers with motion investigation initiatives, revealing motion features and mechanisms in both healthy subjects and patients suffering from hand dysfunctions. Technical advancements have led to the maturation of motion capturing methods such as goniometric gloves. In this project, the CyberGlove as a manufactured product was chosen as a potential tool for the development of a hand function assessment system that would ultimately distinguish between healthy subjects and patients suffering from hand dysfunctions. In this study, the evaluation of the CyberGlove as a feasible clinical tool and its technical adaptations were done in parallel. The sensor output characteristics were investigated using X-ray photography as a spatial golden standard and the sensors were shown to exhibit linear qualities with optimal nonlinearities at 0.6%. The measurement sensitivity and accuracy by the CyberGlove was improved by establishing a calibration protocol suiting the sensor characteristics. Through a calibration protocol using calibration tools made by thermoplastics, the angular measurement error was found to decrease from 7.2% to 1.2%. The technical development of the software part of the project involved the inclusion of data preprocessing, display and analysis modules. To investigate the motion exhibited by healthy subjects, 32 healthy subjects were recruited and they were asked to complete a series of motion according to a designed motion protocol involving a static trial, a timed-grip trial and a rapid-grip trial. Motion features were extracted from recorded motion data by identification and quantification of temporal or spatial characteristics in motion such as joint sequence of events, angular kinematics, finger tip path features and phase diagram features. Some features were evaluated by pattern correlation analysis by linear regression, and healthy subjects all shared similar patterns resulting in high levels of regression coefficients R2 and low levels of slope deviations m. The establishment of motion features along with a prototype motion measurement system allows the continuous development on the CyberGlove as a hand function assessment tool when supported by later clinical adaptations or studies.
DegreeMaster of Philosophy
SubjectHand - Movements.
Hand - Computer simulation.
Dept/ProgramOrthopaedics and Traumatology

 

DC FieldValueLanguage
dc.contributor.advisorLuk, KDK-
dc.contributor.advisorHu, Y-
dc.contributor.advisorTo, MKT-
dc.contributor.authorAu, Kin-hei, Timothy.-
dc.contributor.author區建熙.-
dc.date.issued2012-
dc.identifier.citationAu, K. T. [區建熙]. (2012). Quantitative assessment of hand function by hand motion analysis using cyberglove. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b4784998-
dc.description.abstractHand motion analysis methods have been providing researchers with motion investigation initiatives, revealing motion features and mechanisms in both healthy subjects and patients suffering from hand dysfunctions. Technical advancements have led to the maturation of motion capturing methods such as goniometric gloves. In this project, the CyberGlove as a manufactured product was chosen as a potential tool for the development of a hand function assessment system that would ultimately distinguish between healthy subjects and patients suffering from hand dysfunctions. In this study, the evaluation of the CyberGlove as a feasible clinical tool and its technical adaptations were done in parallel. The sensor output characteristics were investigated using X-ray photography as a spatial golden standard and the sensors were shown to exhibit linear qualities with optimal nonlinearities at 0.6%. The measurement sensitivity and accuracy by the CyberGlove was improved by establishing a calibration protocol suiting the sensor characteristics. Through a calibration protocol using calibration tools made by thermoplastics, the angular measurement error was found to decrease from 7.2% to 1.2%. The technical development of the software part of the project involved the inclusion of data preprocessing, display and analysis modules. To investigate the motion exhibited by healthy subjects, 32 healthy subjects were recruited and they were asked to complete a series of motion according to a designed motion protocol involving a static trial, a timed-grip trial and a rapid-grip trial. Motion features were extracted from recorded motion data by identification and quantification of temporal or spatial characteristics in motion such as joint sequence of events, angular kinematics, finger tip path features and phase diagram features. Some features were evaluated by pattern correlation analysis by linear regression, and healthy subjects all shared similar patterns resulting in high levels of regression coefficients R2 and low levels of slope deviations m. The establishment of motion features along with a prototype motion measurement system allows the continuous development on the CyberGlove as a hand function assessment tool when supported by later clinical adaptations or studies.-
dc.languageeng-
dc.publisherThe University of Hong Kong (Pokfulam, Hong Kong)-
dc.relation.ispartofHKU Theses Online (HKUTO)-
dc.rightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.source.urihttp://hub.hku.hk/bib/B47849988-
dc.subject.lcshHand - Movements.-
dc.subject.lcshHand - Computer simulation.-
dc.titleQuantitative assessment of hand function by hand motion analysis usingcyberglove-
dc.typePG_Thesis-
dc.identifier.hkulb4784998-
dc.description.thesisnameMaster of Philosophy-
dc.description.thesislevelMaster-
dc.description.thesisdisciplineOrthopaedics and Traumatology-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.5353/th_b4784998-
dc.date.hkucongregation2012-

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