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dc.contributor.authorAltun, Keremen_US
dc.contributor.authorBarshan, Billuren_US
dc.coverage.spatialIstanbul, Turkeyen_US
dc.date.accessioned2016-02-08T12:23:13Z
dc.date.available2016-02-08T12:23:13Z
dc.date.issued2010en_US
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11693/28535
dc.descriptionConference name: First International Workshop, HBU 2010en_US
dc.descriptionDate of Conference: August 22, 2010en_US
dc.description.abstractThis paper provides a comparative study on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). Daily and sports activities are classified using five sensor units worn by eight subjects on the chest, the arms, and the legs. Each sensor unit comprises a triaxial gyroscope, a triaxial accelerometer, and a triaxial magnetometer. Principal component analysis (PCA) and sequential forward feature selection (SFFS) methods are employed for feature reduction. For a small number of features, SFFS demonstrates better performance and should be preferable especially in real-time applications. The classifiers are validated using different cross-validation techniques. Among the different classifiers we have considered, BDM results in the highest correct classification rate with relatively small computational cost. © 2010 Springer-Verlag Berlin Heidelberg.en_US
dc.language.isoEnglishen_US
dc.source.titleHuman Behavior Understandingen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/978-3-642-14715-9_5en_US
dc.relation.isversionofhttps://doi.org/10.1007/978-3-642-14715-9en_US
dc.subjectClassification ratesen_US
dc.subjectClassification techniqueen_US
dc.subjectComparative studiesen_US
dc.subjectComputational costsen_US
dc.subjectCross-validation techniqueen_US
dc.subjectDynamic time warpingen_US
dc.subjectFeature reductionen_US
dc.subjectFeature selectionen_US
dc.subjectfeature selection and reductionen_US
dc.subjectHuman activitiesen_US
dc.subjectHuman activity recognitionen_US
dc.subjectInertial sensoren_US
dc.subjectK-nearest neighbor algorithmen_US
dc.subjectLeast squares methodsen_US
dc.subjectReal-time applicationen_US
dc.subjectSensor unitsen_US
dc.subjectSports activityen_US
dc.subjectTri-axial magnetometeren_US
dc.subjectTriaxial accelerometeren_US
dc.subjectBayesian networksen_US
dc.subjectBehavioral researchen_US
dc.subjectClassifiersen_US
dc.subjectDecision makingen_US
dc.subjectInertial navigation systemsen_US
dc.subjectIntelligent agentsen_US
dc.subjectMagnetometersen_US
dc.subjectNeural networksen_US
dc.subjectPrincipal component analysisen_US
dc.subjectSensorsen_US
dc.subjectSupport vector machinesen_US
dc.subjectFeature extractionen_US
dc.titleHuman activity recognition using inertial/magnetic sensor unitsen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.citation.spage38en_US
dc.citation.epage51en_US
dc.citation.volumeNumber6219en_US
dc.identifier.doi10.1007/978-3-642-14715-9_5en_US
dc.identifier.doi10.1007/978-3-642-14715-9en_US
dc.publisherSpringer, Berlin, Heidelbergen_US


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