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dc.contributor.authorAyrulu-Erdem, B.en_US
dc.contributor.authorBarshan, B.en_US
dc.date.accessioned2016-02-08T09:54:31Z
dc.date.available2016-02-08T09:54:31Z
dc.date.issued2011en_US
dc.identifier.issn14248220
dc.identifier.urihttp://hdl.handle.net/11693/22032
dc.description.abstractWe extract the informative features of gyroscope signals using the discrete wavelet transform (DWT) decomposition and provide them as input to multi-layer feed-forward artificial neural networks (ANNs) for leg motion classification. Since the DWT is based on correlating the analyzed signal with a prototype wavelet function, selection of the wavelet type can influence the performance of wavelet-based applications significantly. We also investigate the effect of selecting different wavelet families on classification accuracy and ANN complexity and provide a comparison between them. The maximum classification accuracy of 97.7% is achieved with the Daubechies wavelet of order 16 and the reverse bi-orthogonal (RBO) wavelet of order 3.1, both with similar ANN complexity. However, the RBO 3.1 wavelet is preferable because of its lower computational complexity in the DWT decomposition and reconstruction. © 2011 by the authors; licensee MDPI, Basel, Switzerland.en_US
dc.language.isoEnglishen_US
dc.source.titleSensorsen_US
dc.relation.isversionof10.3390/s110201721en_US
dc.subjectAccelerometersen_US
dc.subjectArtificial neural networksen_US
dc.subjectDiscrete wavelet transformen_US
dc.subjectFeature extractionen_US
dc.subjectGyroscopesen_US
dc.subjectInertial sensorsen_US
dc.subjectLeg motion classificationen_US
dc.subjectPattern recognitionen_US
dc.subjectWavelet decompositionen_US
dc.subjectalgorithmen_US
dc.subjectarticleen_US
dc.subjectartificial neural networken_US
dc.subjecthumanen_US
dc.subjectinstrumentationen_US
dc.subjectlegen_US
dc.subjectmotionen_US
dc.subjectphysiologyen_US
dc.subjectsignal processingen_US
dc.subjectwavelet analysisen_US
dc.subjectAlgorithmsen_US
dc.subjectHumansen_US
dc.subjectLegen_US
dc.subjectMotionen_US
dc.subjectNeural Networks (Computer)en_US
dc.subjectSignal Processing, Computer-Assisteden_US
dc.subjectWavelet Analysisen_US
dc.titleLeg motion classification with artificial neural networks using wavelet-based features of gyroscope signalsen_US
dc.typeArticleen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.citation.spage1721en_US
dc.citation.epage1743en_US
dc.citation.volumeNumber11en_US
dc.citation.issueNumber2en_US
dc.identifier.doi10.3390/s110201721en_US


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