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deepGesture: Deep learning-based gesture recognition scheme using motion sensors

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DC FieldValueLanguage
dc.contributor.authorKim, Ji-Hae-
dc.contributor.authorHong, Gwang-Soo-
dc.contributor.authorKim, Byung-Gyu-
dc.contributor.authorDogra, Debi P.-
dc.date.available2021-02-22T07:46:19Z-
dc.date.created2020-08-18-
dc.date.issued2018-12-
dc.identifier.issn0141-9382-
dc.identifier.urihttps://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/4141-
dc.description.abstractRecent advancement in smart phones and sensor technology has promoted research in gesture recognition. This has made designing of efficient gesture interface easy. However, human activity recognition (HAR) through gestures is not trivial since each person may pose the same gesture differently. In this paper, we propose deepGesture algorithm, a new arm gesture recognition method based on gyroscope and accelerometer sensors using deep convolution and recurrent neural networks. This method uses four deep convolution layers to automate feature learning in raw sensor data. The features of the convolution layers are used as input of the gated recurrent unit (GRU) which is based on the state-of-the-art recurrent neural network (RNN) structure to capture long-term dependency and model sequential data. The input data of the proposed algorithm is obtained through motion sequence data extracted using a wrist-type smart band device equipped with gyroscope and accelerometer sensors. The data is initially segmented in fixed length segments. The segmented data is labeled and we construct the database. Then the labeled data is used in our learning algorithm. To verify the applicability of the algorithm, several experiments have been performed to measure the accuracy of gesture classification. Compared to the human activity recognition method, our experimental results show that the proposed deepGesture algorithm can increase the average Fl-score for recognition of nine defined arm gestures by 6%.-
dc.language영어-
dc.language.isoen-
dc.publisherELSEVIER SCIENCE BV-
dc.titledeepGesture: Deep learning-based gesture recognition scheme using motion sensors-
dc.typeArticle-
dc.contributor.affiliatedAuthorKim, Byung-Gyu-
dc.identifier.doi10.1016/j.displa.2018.08.001-
dc.identifier.scopusid2-s2.0-85052750614-
dc.identifier.wosid000454376200006-
dc.identifier.bibliographicCitationDISPLAYS, v.55, pp.38 - 45-
dc.relation.isPartOfDISPLAYS-
dc.citation.titleDISPLAYS-
dc.citation.volume55-
dc.citation.startPage38-
dc.citation.endPage45-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaInstruments & Instrumentation-
dc.relation.journalResearchAreaOptics-
dc.relation.journalWebOfScienceCategoryComputer Science, Hardware & Architecture-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.relation.journalWebOfScienceCategoryInstruments & Instrumentation-
dc.relation.journalWebOfScienceCategoryOptics-
dc.subject.keywordPlusNEURAL-NETWORKS-
dc.subject.keywordAuthorHuman activity recognition-
dc.subject.keywordAuthorWearable sensors-
dc.subject.keywordAuthorDeep learning-
dc.subject.keywordAuthorGRU-
dc.subject.keywordAuthorNeural network-
dc.identifier.urlhttps://www.sciencedirect.com/science/article/abs/pii/S0141938217302032?via%3Dihub-
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