Spine Posture Detection for Office Workers With Hybrid Machine Learning

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dc.contributor.author Öke, Deniz
dc.contributor.author Çakar, Tuna
dc.contributor.author Yıldız, Ahmet
dc.contributor.author Mise, Pelin
dc.contributor.author Terzibaşıoğlu, Aynur Metin
dc.date.accessioned 2023-12-13T09:10:19Z
dc.date.available 2023-12-13T09:10:19Z
dc.date.issued 2023
dc.description.abstract This 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.
dc.identifier.citation Yildiz, 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).
dc.identifier.doi 10.1109/UBMK59864.2023.10286584
dc.identifier.isbn 9798350340815
dc.identifier.scopus 2-s2.0-85177551231
dc.identifier.uri https://doi.org/10.1109/UBMK59864.2023.10286584
dc.identifier.uri https://hdl.handle.net/20.500.11779/2146
dc.language.iso en
dc.publisher IEEE
dc.relation.ispartof 2023 8th International Conference on Computer Science and Engineering (UBMK)
dc.rights info:eu-repo/semantics/closedAccess
dc.subject Office workers
dc.subject Sitting posture
dc.subject Machine learning
dc.subject Spine posture
dc.subject Light gradient boosting machine
dc.title Spine Posture Detection for Office Workers With Hybrid Machine Learning
dc.type Conference Object
dspace.entity.type Publication
gdc.author.id Tuna Çakar / 0000-0001-8594-7399
gdc.author.institutional Çakar, Tuna
gdc.bip.impulseclass C5
gdc.bip.influenceclass C5
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gdc.coar.access metadata only access
gdc.coar.type text::conference output
gdc.description.department Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
gdc.description.endpage 491
gdc.description.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
gdc.description.scopusquality N/A
gdc.description.startpage 486
gdc.description.wosquality N/A
gdc.identifier.openalex W4387913091
gdc.index.type Scopus
gdc.oaire.diamondjournal false
gdc.oaire.impulse 0.0
gdc.oaire.influence 2.5942106E-9
gdc.oaire.isgreen false
gdc.oaire.popularity 2.5427536E-9
gdc.oaire.publicfunded false
gdc.openalex.collaboration National
gdc.openalex.fwci 0.0
gdc.openalex.normalizedpercentile 0.2
gdc.opencitations.count 0
gdc.plumx.mendeley 1
gdc.plumx.scopuscites 0
gdc.publishedmonth Eylül
gdc.relation.journal 8th International Conference on Computer Science and Engineering - UBMK 2023
gdc.scopus.citedcount 0
gdc.virtual.author Çakar, Tuna
gdc.wos.publishedmonth Eylül
gdc.yokperiod YÖK - 2023-24
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