Comparative study on classifying human activities with miniature inertial and magnetic sensors

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dc.citation.epage3620en_US
dc.citation.issueNumber10en_US
dc.citation.spage3605en_US
dc.citation.volumeNumber43en_US
dc.contributor.authorAltun, K.en_US
dc.contributor.authorBarshan, B.en_US
dc.contributor.authorTunçel, O.en_US
dc.date.accessioned2016-02-08T09:56:49Z
dc.date.available2016-02-08T09:56:49Z
dc.date.issued2010en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_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), a rule-based algorithm (RBA) or decision tree, 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). 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. A performance comparison of the classification techniques is provided in terms of their correct differentiation rates, confusion matrices, and computational cost, as well as their pre-processing, training, and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that in general, BDM results in the highest correct classification rate with relatively small computational cost.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T09:56:49Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2010en
dc.identifier.doi10.1016/j.patcog.2010.04.019en_US
dc.identifier.issn0031-3203
dc.identifier.urihttp://hdl.handle.net/11693/22200
dc.language.isoEnglishen_US
dc.publisherElsevieren_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.patcog.2010.04.019en_US
dc.source.titlePattern Recognitionen_US
dc.subjectAccelerometeren_US
dc.subjectActivity recognition and classificationen_US
dc.subjectArtificial neural networksen_US
dc.subjectBayesian decision makingen_US
dc.subjectDecision treeen_US
dc.subjectDynamic time warpingen_US
dc.subjectFeature extractionen_US
dc.subjectFeature reductionen_US
dc.subjectGyroscopeen_US
dc.subjectInertial sensorsen_US
dc.subjectk-Nearest neighboren_US
dc.subjectLeast-squares methoden_US
dc.subjectMagnetometeren_US
dc.subjectRule-based algorithmen_US
dc.subjectSupport vector machinesen_US
dc.titleComparative study on classifying human activities with miniature inertial and magnetic sensorsen_US
dc.typeArticleen_US

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