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dc.contributor.authorYazar, A.en_US
dc.contributor.authorKeskin F.en_US
dc.contributor.authorTöreyin, B. U.en_US
dc.contributor.authorÇetin, A. E.en_US
dc.date.accessioned2016-02-08T09:41:16Z
dc.date.available2016-02-08T09:41:16Z
dc.date.issued2013en_US
dc.identifier.issn0167-8655
dc.identifier.urihttp://hdl.handle.net/11693/21106
dc.description.abstractThe goal of Ambient Assisted Living (AAL) research is to improve the quality of life of the elderly and handicapped people and help them maintain an independent lifestyle with the use of sensors, signal processing and telecommunications infrastructure. Unusual human activity detection such as fall detection has important applications. In this paper, a fall detection algorithm for a low cost AAL system using vibration and passive infrared (PIR) sensors is proposed. The single-tree complex wavelet transform (ST-CWT) is used for feature extraction from vibration sensor signal. The proposed feature extraction scheme is compared to discrete Fourier transform and mel-frequency cepstrum coefficients based feature extraction methods. Vibration signal features are classified into "fall" and "ordinary activity" classes using Euclidean distance, Mahalanobis distance, and support vector machine (SVM) classifiers, and they are compared to each other. The PIR sensor is used for the detection of a moving person in a region of interest. The proposed system works in real-time on a standard personal computer.en_US
dc.language.isoEnglishen_US
dc.source.titlePattern Recognition Lettersen_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.patrec.2012.12.010en_US
dc.subjectFalling person detectionen_US
dc.subjectFeature extractionen_US
dc.subjectPIR sensoren_US
dc.subjectSingle-tree complex wavelet transformen_US
dc.subjectSupport vector machinesen_US
dc.subjectVibration sensoren_US
dc.subjectAmbient assisted living (AAL)en_US
dc.subjectComplex wavelet transformsen_US
dc.subjectFeature extraction methodsen_US
dc.subjectMel frequency cepstrum coefficientsen_US
dc.subjectPerson detectionen_US
dc.subjectPir sensorsen_US
dc.subjectTelecommunications infrastructuresen_US
dc.subjectVibration sensorsen_US
dc.subjectDiscrete Fourier transformsen_US
dc.subjectFeature extractionen_US
dc.subjectImage segmentationen_US
dc.subjectPersonal computersen_US
dc.subjectSensorsen_US
dc.subjectSupport vector machinesen_US
dc.subjectVentilation exhaustsen_US
dc.subjectWavelet transformsen_US
dc.subjectForestryen_US
dc.titleFall detection using single-tree complex wavelet transformen_US
dc.typeArticleen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.citation.spage1945en_US
dc.citation.epage1952en_US
dc.citation.volumeNumber34en_US
dc.citation.issueNumber15en_US
dc.identifier.doi10.1016/j.patrec.2012.12.010en_US
dc.publisherElsevieren_US


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