Classifying human leg motions with uniaxial piezoelectric gyroscopes

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dc.citation.epage8546en_US
dc.citation.issueNumber11en_US
dc.citation.spage8508en_US
dc.citation.volumeNumber9en_US
dc.contributor.authorTunçel O.en_US
dc.contributor.authorAltun, K.en_US
dc.contributor.authorBarshan, B.en_US
dc.date.accessioned2016-02-08T10:01:58Z
dc.date.available2016-02-08T10:01:58Z
dc.date.issued2009en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractThis paper provides a comparative study on the different techniques of classifying human leg motions that are performed using two low-cost uniaxial piezoelectric gyroscopes worn on the leg. A number of feature sets, extracted from the raw inertial sensor data in different ways, are used in the classification process. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, least-squares method (LSM), k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). A performance comparison of these classification techniques is provided in terms of their correct differentiation rates, confusion matrices, computational cost, and training and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that BDM, in general, results in the highest correct classification rate with relatively small computational cost. © 2009 by the authors.en_US
dc.identifier.doi10.3390/s91108508en_US
dc.identifier.issn14248220
dc.identifier.urihttp://hdl.handle.net/11693/22581
dc.language.isoEnglishen_US
dc.relation.isversionof10.3390/s91108508en_US
dc.source.titleSensorsen_US
dc.subjectArtificial neural networksen_US
dc.subjectBayesian decision makingen_US
dc.subjectDynamic time warpingen_US
dc.subjectGyroscopeen_US
dc.subjectInertial sensorsen_US
dc.subjectK-nearest neighboren_US
dc.subjectLeast-squares methoden_US
dc.subjectMotion classificationen_US
dc.subjectRule-based algorithmen_US
dc.subjectSupport vector machinesen_US
dc.subjectBayesian decision makingsen_US
dc.subjectDynamic time warpingen_US
dc.subjectInertial sensoren_US
dc.subjectK-nearest neighborsen_US
dc.subjectLeast squares methodsen_US
dc.subjectMotion classificationen_US
dc.subjectRule based algorithmsen_US
dc.subjectAlgorithmsen_US
dc.subjectCostsen_US
dc.subjectDecision treesen_US
dc.subjectDigital storageen_US
dc.subjectGyroscopesen_US
dc.subjectInertial navigation systemsen_US
dc.subjectNeural networksen_US
dc.subjectPattern recognitionen_US
dc.subjectPiezoelectricityen_US
dc.subjectSupport vector machinesen_US
dc.subjectDecision makingen_US
dc.titleClassifying human leg motions with uniaxial piezoelectric gyroscopesen_US
dc.typeArticleen_US

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