Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11779/2146
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dc.contributor.authorYıldız, Ahmet-
dc.contributor.authorMise, Pelin-
dc.contributor.authorÇakar, Tuna-
dc.contributor.authorTerzibaşıoğlu, Aynur Metin-
dc.contributor.authorÖke, Deniz-
dc.date.accessioned2023-12-13T09:10:19Z-
dc.date.available2023-12-13T09:10:19Z-
dc.date.issued2023-
dc.identifier.citationYildiz, A., Mise, P., Cakar, T., Terzibasioglu, A. M., & Oke, D. (2023, September). Spine posture detection for office workers with hybrid machine learning. In 2023 8th International Conference on Computer Science and Engineering (UBMK). (pp. 486-491).en_US
dc.identifier.isbn979-835034081-5-
dc.identifier.urihttps://doi.org/10.1109/UBMK59864.2023.10286584-
dc.identifier.urihttps://hdl.handle.net/20.500.11779/2146-
dc.description.abstractThis study aims to detect bad spine posture using an al-ternative approach that doesn't rely on deep learning or excessive energy. The goal is to improve accuracy and effectiveness without disrupting workflow. A custom dataset was created, numerical inferences were made from posture values, and a hybrid approach using Light Gradient Boosting achieved a 96 % success rate.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectlight gradient boosting machineen_US
dc.subjectmachine learningen_US
dc.subjectoffice workersen_US
dc.subjectsitting postureen_US
dc.subjectspine postureen_US
dc.titleSpine posture detection for office workers with hybrid machine learningen_US
dc.typeConference Objecten_US
dc.identifier.doi10.1109/UBMK59864.2023.10286584-
dc.identifier.scopus2-s2.0-85177551231en_US
dc.authoridTuna Çakar / 0000-0001-8594-7399-
dc.description.PublishedMonthEylülen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.endpage491en_US
dc.identifier.startpage486en_US
dc.departmentMühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.relation.journal8th International Conference on Computer Science and Engineering - UBMK 2023en_US
dc.institutionauthorYıldız, Ahmet-
dc.institutionauthorMişe, Pelin-
dc.institutionauthorÇakar, Tuna-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextembargo_20400101-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
item.openairetypeConference Object-
crisitem.author.dept02.02. Department of Computer Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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