Turkish sign language recognition using fuzzy logic asisted ELM and CNN methods

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dc.contributor.authorSonugur, Guray
dc.contributor.authorCayli, Abdullah
dc.date.accessioned2024-09-29T16:07:58Z
dc.date.available2024-09-29T16:07:58Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractThis work aimed to develop a data glove for the real-time translation of Turkish sign language. In addition, a novel Fuzzy Logic Assisted ELM method (FLA-ELM) for hand gesture classification is proposed. In order to acquire motion information from the gloves, 12 flexibility sensors, two inertial sensors, and 10 Hall sensors were employed. The NVIDIA Jetson Nano, a small pocketable minicomputer, was used to run the recognition software. A total of 34 signal information was gathered from the sensors, and feature matrices were generated in the form of time series for each word. In addition, an algorithm based on Euclidean distance has been developed to detect end-points between adjacent words in a sentence. In addition to the proposed method, CNN and classical ANN methods, whose model was created by us, were used in sign language recognition experiments, and the results were compared. For each classified word, samples were collected from 25 different signers, and 3000 sample data were obtained for 120 words. Furthermore, the dataset's size was reduced using PCA, and the results of the newly created datasets were compared to the reference results. In the performance tests, single words and three-word sentences were translated with an accuracy of up to 96.8% and a minimum 2.4 ms processing time.en_US
dc.description.sponsorshipAfyon Kocatepe University, Scientific Research Projects Council [19, FEN.B.IL.33]en_US
dc.description.sponsorshipThis research was supported by Afyon Kocatepe University, Scientific Research Projects Council with Project Number 19.FEN.B.IL.33en_US
dc.identifier.doi10.3233/JIFS-231601
dc.identifier.endpage8565en_US
dc.identifier.issn1064-1246
dc.identifier.issn1875-8967
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85176600613en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage8553en_US
dc.identifier.urihttps://doi.org/10.3233/JIFS-231601
dc.identifier.urihttps://hdl.handle.net/20.500.14619/7286
dc.identifier.volume45en_US
dc.identifier.wosWOS:001099536200090en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIos Pressen_US
dc.relation.ispartofJournal of Intelligent & Fuzzy Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectExtreme learning machines (ELM)en_US
dc.subjectfuzzy logicen_US
dc.subjectsign language recognitionen_US
dc.subjectdata gloveen_US
dc.subjectCNNen_US
dc.subjectANNen_US
dc.titleTurkish sign language recognition using fuzzy logic asisted ELM and CNN methodsen_US
dc.typeArticleen_US

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