Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11779/2146| Title: | Spine Posture Detection for Office Workers With Hybrid Machine Learning | Authors: | Öke, Deniz Çakar, Tuna Yıldız, Ahmet Mise, Pelin Terzibaşıoğlu, Aynur Metin |
Keywords: | Office workers Sitting posture Machine learning Spine posture Light gradient boosting machine |
Publisher: | IEEE | Source: | 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). | 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. | URI: | https://doi.org/10.1109/UBMK59864.2023.10286584 https://hdl.handle.net/20.500.11779/2146 |
ISBN: | 9798350340815 |
| Appears in Collections: | Bilgisayar Mühendisliği Bölümü Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 232131232132.pdf Until 2040-01-01 | Proceedings Paper | 468.75 kB | Adobe PDF | View/Open Request a copy |
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