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dc.contributor.advisorBarshan, Billur
dc.contributor.authorTunçel, Orkun
dc.date.accessioned2016-01-08T18:18:14Z
dc.date.available2016-01-08T18:18:14Z
dc.date.issued2009
dc.identifier.urihttp://hdl.handle.net/11693/15415
dc.descriptionAnkara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2009.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2009.en_US
dc.descriptionIncludes bibliographical references leaves 79-92.en_US
dc.description.abstractThis thesis provides a comparative study on activity recognition using miniature inertial sensors (gyroscopes and accelerometers) and magnetometers worn on the human body. The classification methods used and compared in this study are: a rule-based algorithm (RBA) or decision tree, least-squares method (LSM), k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW- 1 and DTW-2), and support vector machines (SVM). In the first part of this study, eight different leg motions are classified using only two single-axis gyroscopes. In the second part, human activities are classified using five sensor units worn on different parts of the body. Each sensor unit comprises a tri-axial gyroscope, a tri-axial accelerometer and a tri-axial magnetometer. Different feature sets extracted from the raw sensor data and these are used in the classification process. A number of feature extraction and reduction techniques (principal component analysis) as well as different cross-validation techniques have been implemented and compared. A performance comparison of these classification methods is provided in terms of their correct differentiation rates, confusion matrices, pre-processing and training times and classification times. Among the classification techniques we have considered and implemented, SVM, in general, gives the highest correct differentiation rate, followed by k-NN. The classification time for RBA is the shortest, followed by SVM or LSM, k-NN or DTW-1, and DTW-2 methods. SVM requires the longest training time, whereas DTW-2 takes the longest amount of classification time. Although there is not a significant difference between the correct differentiation rates obtained by different crossvalidation techniques, repeated random sub-sampling uses the shortest amount of classification time, whereas leave-one-out requires the longest.en_US
dc.description.statementofresponsibilityTunçel, Orkunen_US
dc.format.extentxiv, 92 leaves, illustrationsen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectinertial sensorsen_US
dc.subjectgyroscopeen_US
dc.subjectaccelerometeren_US
dc.subjectmagnetometeren_US
dc.subjecthuman activity recognitionen_US
dc.subjectmotion classificationen_US
dc.subjectpattern recognitionen_US
dc.subjectfeatureen_US
dc.subjectprincipal component analysisen_US
dc.subjectcross-validationen_US
dc.subjectrule-based algorithmen_US
dc.subjectdecision treeen_US
dc.subjectleastsquares methoden_US
dc.subjectk-nearest neighboren_US
dc.subjectdynamic time warpingen_US
dc.subjectsupport vector machinesen_US
dc.subject.lccTA1650 .T85 2009en_US
dc.subject.lcshOptical pattern recognition.en_US
dc.subject.lcshComputer vision.en_US
dc.subject.lcshImage processing--Digital techniques.en_US
dc.subject.lcshBody, Human--Computer simulation.en_US
dc.subject.lcshSensors.en_US
dc.subject.lcshHuman locomotion.en_US
dc.titleHuman activity classification with miniature inertial sensorsen_US
dc.typeThesisen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US


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