Analysis of motor skills in subjects with Down's Syndrome using computer vision techniques

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dc.contributor.authorSvendsen, Jeremy Paul
dc.contributor.supervisorBranzan Albu, Alexandra
dc.date.accessioned2010-06-02T18:36:07Z
dc.date.available2010-06-02T18:36:07Z
dc.date.copyright2009en
dc.date.issued2010-06-02T18:36:07Z
dc.degree.departmentDepartment of Electrical and Computer Engineering
dc.degree.levelMaster of Applied Science M.A.Sc.en
dc.description.abstractComputer vision techniques for human motion analysis have the potential to significantly improve the monitoring of motor rehabilitation processes. With respect to traditional marker-based techniques, computer vision offers both portability and low-cost. This thesis describes methods that have been designed for the analysis of the motor skills of subjects with Down's syndrome. More specifically, the motion of interest is weight-shifting; this motion plays an important role in the safety of locomotory activities, as well as of other daily actions. From a theoretical viewpoint, the thesis proposes several new concepts for human motion analysis and describes their algorithmic implementation, as well as their applicability to the detection and description of several motion primitives. The thesis introduces the concept of curved bounding box, which is an extension of the rectangular bounding box that is typically used for detection and tracking of rigid motion. This concept is successfully applied to the detection of deformable motion, such as arm, knee and upper body motions. A new technique for identifying subject-representative patterns of motion is also proposed. This technique is based on Motion History Images, which hold both analytical and visualization power.en
dc.identifier.urihttp://hdl.handle.net/1828/2834
dc.languageEnglisheng
dc.language.isoenen
dc.rightsAvailable to the World Wide Weben
dc.subjectComputer visionen
dc.subjectDown Syndromeen
dc.subject.lcshUVic Subject Index::Sciences and Engineering::Applied Sciences::Computer scienceen
dc.titleAnalysis of motor skills in subjects with Down's Syndrome using computer vision techniquesen
dc.typeThesisen

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