Show simple item record

dc.contributor.advisorBarshan, Billur
dc.contributor.authorYüksek, Murat Cihan
dc.date.accessioned2016-01-08T18:21:27Z
dc.date.available2016-01-08T18:21:27Z
dc.date.issued2011
dc.identifier.urihttp://hdl.handle.net/11693/15616
dc.descriptionAnkara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Science of Bilkent University, 2011.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2011.en_US
dc.descriptionIncludes bibliographical references leaves 57-67.en_US
dc.description.abstractThis study provides a comparative assessment on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques compared in this study are: naive Bayesian (NB) classifier, artificial neural networks (ANNs), dissimilarity-based classifier (DBC), various decision-tree methods, Gaussian mixture model (GMM), and support vector machines (SVM). The algorithms for these techniques are provided on two commonly used open source environments: Waikato environment for knowledge analysis (WEKA), a Java-based software; and pattern recognition toolbox (PRTools), a MATLAB toolbox. Human activities are classified using five sensor units worn on the chest, the arms, and the legs. Each sensor unit comprises a tri-axial gyroscope, a tri-axial accelerometer, and a tri-axial magnetometer. A feature set extracted from the raw sensor data using principal component analysis (PCA) is used in the classification process. Three different cross-validation techniques are employed to validate the classifiers. A performance comparison of the classification techniques is provided in terms of their correct differentiation rates, confusion matrices, and computational cost. The methods that result in the highest correct differentiation rates are found to be ANN (99.2%), SVM (99.2%), and GMM (99.1%). The magnetometer is the best type of sensor to be used in classification whereas gyroscope is the least useful. Considering the locations of the sensor units on body, the sensors worn on the legs seem to provide the most valuable information.en_US
dc.description.statementofresponsibilityYüksek, Murat Cihanen_US
dc.format.extentxv, 67 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.subjectactivity recognition and classificationen_US
dc.subjectfeature extraction and reductionen_US
dc.subjectcross validationen_US
dc.subjectBayesian decision makingen_US
dc.subjectartificial neural networksen_US
dc.subjectsupport vector machinesen_US
dc.subjectdecision treesen_US
dc.subjectdissimilarity-based classifieren_US
dc.subjectGaussian mixture modelen_US
dc.subjectWEKAen_US
dc.subjectPRToolsen_US
dc.subject.lccTA1650 .Y85 2011en_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.subject.lcshIntelligent control systems.en_US
dc.subject.lcshDetectors.en_US
dc.titleA comparative study on human activity classification with miniature inertial and magnetic sensorsen_US
dc.typeThesisen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record