Öke, DenizÇakar, TunaYıldız, AhmetMise, PelinTerzibaşıoğlu, Aynur Metin2023-12-132023-12-132023Yildiz, 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).9798350340815https://doi.org/10.1109/UBMK59864.2023.10286584https://hdl.handle.net/20.500.11779/2146This 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.eninfo:eu-repo/semantics/closedAccessOffice workersSitting postureMachine learningSpine postureLight gradient boosting machineConference Object10.1109/UBMK59864.2023.102865842-s2.0-85177551231