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

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.