Spine Posture Detection for Office Workers With Hybrid Machine Learning

Loading...
Thumbnail Image

Date

2023

Journal Title

Journal ISSN

Volume Title

Publisher

IEEE

Open Access Color

Green Open Access

No

OpenAIRE Downloads

OpenAIRE Views

Publicly Funded

No
Impulse
Average
Influence
Average
Popularity
Average

Research Projects

Journal Issue

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

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

Scopus Q

N/A
OpenCitations Logo
OpenCitations Citation Count
N/A

Source

2023 8th International Conference on Computer Science and Engineering (UBMK)

Volume

Issue

Start Page

486

End Page

491
PlumX Metrics
Citations

Scopus : 0

Captures

Mendeley Readers : 1

Google Scholar Logo
Google Scholar™
OpenAlex Logo
OpenAlex FWCI
0.0

Sustainable Development Goals

16

PEACE, JUSTICE AND STRONG INSTITUTIONS
PEACE, JUSTICE AND STRONG INSTITUTIONS Logo