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Advancing Workplace Safety: A Proactive Approach with Convolutional Neural Network for Hand Pose Estimation in Press Machine Operations

Şuayip Aykut Atmaca1✉,
Hüseyin Hamad2,
Burcu Çağlar Gençosman3
1Mert Yazılım ve Elektronik
2Mert Yazılım ve Elektronik
3Uludağ Üniversitesi
Published:December 31, 2023
DOI: 10.56038/ejrnd.v3i4.297
Vol. 3, No. 4

Abstract

Press machine operations are integral to goods production across industries, yet worker safety faces significant risks. Machine misuse and non-compliance with safety standards contribute substantially to these incidents. This study addresses the mounting concerns regarding workplace incidents through a proactive solution—a Convolutional Neural Network (CNN) model crafted to prevent press machine misuse by monitoring workers' hand placement during operation. The model that we suggest ensures adherence to safety standards. The CNN model does not replace the role of human operators but acts as a supportive layer, providing instant feedback and intervention when deviations from safety standards are detected. In conclusion, this research endeavors to pave the way for a safer and more secure industrial environment by leveraging the capabilities of advanced technology. The proposed CNN model addresses current concerns and sets a precedent for future advancements in ensuring workplace safety across diverse industries.

Keywords
Hand-pose estimationwork safetydeep learningCNNhyperparameter tuning

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Cite This Article
Atmaca, Ş. A., Hamad, H., Gençosman, B. Ç. (2023). Advancing Workplace Safety: A Proactive Approach with Convolutional Neural Network for Hand Pose Estimation in Press Machine Operations. *The European Journal of Research and Development*, 3(4), 66-75. https://doi.org/10.56038/ejrnd.v3i4.297

Bibliographic Info

JournalThe European Journal of Research and Development
Volume3
Issue4
Pages66–75
PublishedDecember 31, 2023
eISSN2822-2296