Spine Posture Detection for Office Workers With Hybrid Machine Learning
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Date
2023
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Open Access Color
Green Open Access
No
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Publicly Funded
No
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.
Description
ORCID
Keywords
Office workers, Sitting posture, Machine learning, Spine posture, Light gradient boosting machine
Turkish CoHE Thesis Center URL
Fields of Science
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).
WoS Q
N/A
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N/A

OpenCitations Citation Count
N/A
Source
2023 8th International Conference on Computer Science and Engineering (UBMK)
Volume
Issue
Start Page
486
End Page
491
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Scopus : 0
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OpenAlex FWCI
0.0
Sustainable Development Goals
16
PEACE, JUSTICE AND STRONG INSTITUTIONS


