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dc.contributor.advisorYanushkevich, Svetlana N.
dc.contributor.authorAbid, Noor
dc.date2019-06
dc.date.accessioned2019-01-28T16:14:48Z
dc.date.available2019-01-28T16:14:48Z
dc.date.issued2019-01-25
dc.identifier.citationAbid, N. (2019). RGB-Depth Based Gait Data Analysis for Asymmetry Detection (Unpublished master's thesis). University of Calgary, Calgary, AB.en_US
dc.identifier.urihttp://hdl.handle.net/1880/109859
dc.description.abstractIn this study, a biometric-based framework is developed to determine human gait abnormality and its severity for application in the healthcare sector for patient status monitoring and pathology diagnosis. The main focus is on detecting the abnormalities, such as asymmetry in gait (antalgic gait). The input data for the proposed framework is provided by non-invasive, no-marker gait data acquisition using the Microsoft Kinect v2 camera. The sample data includes information from a collected database and an externally-sourced database (UPCV). Important gait features are extracted and analyzed through a Dynamic Bayesian Network (DBN) and other traditional classifiers such as Support Vector Machines, K-Nearest-Neighbors and Linear Discriminant Analysis. The DBN-based approach shows better performance and more consistent results than other classifiers. Three separate experiments were conducted. The first experiment examined the feasibility of using lower body joint data (knee angle, ankle angle) to classify gait type. The second experiment examined the use of upper body joint data (shoulder movement) to determine the severity of any abnormalities detected in the first experiment. The third experiment examined how the fusion of both lower body and upper body data impacts the accuracy of decision. Furthermore, a separate experiment was conducted which used the fusion of gait characteristics with facial scores and height measurements to recognize an individual’s identity.en_US
dc.language.isoenen_US
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.en_US
dc.subject.classificationInformation Scienceen_US
dc.subject.classificationComputer Scienceen_US
dc.titleRGB-Depth Based Gait Data Analysis for Asymmetry Detectionen_US
dc.typemaster thesisen_US
dc.publisher.facultySchulich School of Engineeringen_US
dc.publisher.institutionUniversity of Calgaryen
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/36116
thesis.degree.nameMaster of Science (MSc)en_US
thesis.degree.disciplineEngineering – Electrical & Computeren_US
thesis.degree.grantorUniversity of Calgaryen_US
dc.contributor.committeememberQiao, Sun
dc.contributor.committeememberSmith, Michael Richard
dc.contributor.committeememberFar, Behrouz Homayoun
ucalgary.item.requestcopytrue


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University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.