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dc.contributor.authorErdas, C.Berke
dc.contributor.authorAtasoy, Isil
dc.contributor.authorAcici, Koray
dc.contributor.authorOgul, Hasan
dc.date.accessioned2019-06-20T12:24:11Z
dc.date.available2019-06-20T12:24:11Z
dc.date.issued2016
dc.identifier.issn1877-0509
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S1877050916322153?via%3Dihub
dc.identifier.urihttp://hdl.handle.net/11727/3650
dc.description.abstractActivity recognition is the problem of predicting the current action of a person through the motion sensors worn on the body. The problem is usually approached as a supervised classification task where a discriminative model is learned from known samples and a new query is assigned to a known activity label using learned model. The challenging issue here is how to feed this classifier with a fixed number of features where the real input is a raw signal of varying length. In this study, we consider three possible feature sets, namely time-domain, frequency domain and wavelet-domain statistics, and their combinations to represent motion signal obtained from accelerometer reads worn in chest through a mobile phone. In addition to a systematic comparison of these feature sets, we also provide a comprehensive evaluation of some preprocessing steps such as filtering and feature selection. The results determine that feeding a random forest classifier with an ensemble selection of most relevant time-domain and frequency-domain features extracted from raw data can provide the highest accuracy in a real dataset. (C) 2016 The Authors. Published by Elsevier B.V.en_US
dc.language.isoengen_US
dc.relation.isversionof10.1016/j.procs.2016.09.070en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectActivity recognitionen_US
dc.subjectAccelerometer analysisen_US
dc.subjectFeature selectionen_US
dc.titleIntegrating features for accelerometer-based activity recognitionen_US
dc.typeconferenceObjecten_US
dc.relation.journal7TH INTERNATIONAL CONFERENCE ON EMERGING UBIQUITOUS SYSTEMS AND PERVASIVE NETWORKS (EUSPN 2016)/THE 6TH INTERNATIONAL CONFERENCE ON CURRENT AND FUTURE TRENDS OF INFORMATION AND COMMUNICATION TECHNOLOGIES IN HEALTHCARE (ICTH-2016)en_US
dc.identifier.volume98en_US
dc.identifier.startpage522en_US
dc.identifier.endpage527en_US
dc.identifier.wos000387551200074


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