Social touch gesture recognition using convolutional neural network

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dc.contributor.authorAlbawi, Saad
dc.contributor.authorBayat, Oğuz
dc.contributor.authorAl-Azawi, Saad
dc.contributor.authorUçan, Osman Nuri
dc.date.accessioned2021-05-15T12:42:06Z
dc.date.available2021-05-15T12:42:06Z
dc.date.issued2018
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.descriptionAl-Azawi, Saad/0000-0003-2475-3499; albawi, saad Qassim/0000-0002-9111-1210
dc.description.abstractRecently, social touch gesture recognition has been considered an important topic for touch modality, which can lead to highly efficient and realistic human-robot interaction. In this paper, a deep convolutional neural network is selected to implement a social touch recognition system for raw input samples (sensor data) only. The touch gesture recognition is performed using a dataset previously measured with numerous subjects that perform varying social gestures. This dataset is dubbed as the corpus of social touch, where touch was performed on a mannequin arm. A leave-one-subject-out cross-validation method is used to evaluate system performance. The proposed method can recognize gestures in nearly real time after acquiring a minimum number of frames (the average range of frame length was from 0.2% to 4.19% from the original frame lengths) with a classification accuracy of 63.7%. The achieved classification accuracy is competitive in terms of the performance of existing algorithms. Furthermore, the proposed system outperforms other classification algorithms in terms of classification ratio and touch recognition time without data preprocessing for the same dataset.en_US
dc.identifier.doi10.1155/2018/6973103
dc.identifier.issn1687-5265
dc.identifier.issn1687-5273
dc.identifier.pmid30402085
dc.identifier.scopus2-s2.0-85056261329
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1155/2018/6973103
dc.identifier.urihttps://hdl.handle.net/20.500.12939/895
dc.identifier.volume2018en_US
dc.identifier.wosWOS:000447886700001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorUçan, Osman Nuri
dc.institutionauthorBayat, Oğuz
dc.institutionauthorAlbawi, Saad
dc.language.isoen
dc.publisherHindawi Ltden_US
dc.relation.ispartofComputational Intelligence and Neuroscience
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectNeural Networken_US
dc.subjectSocial Touchen_US
dc.subjectEngineeringen_US
dc.titleSocial touch gesture recognition using convolutional neural network
dc.typeArticle

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