Implementing a Risk Assessment System of Electric Welders’ Muscle Injuries for Working Posture Detection with AI Technology
DOI:
https://doi.org/10.3991/ijoe.v20i04.46465Keywords:
machine learning, REBA, risk assessment, ergonomicsAbstract
Maintaining health and safety is essential for workers’ quality of life, and thus, this has become one of the main priorities for industrial enterprises. Electric welders want required safety precautions to be implemented during work in industries with safety risks, especially muscle injuries. This challenge needs to be addressed by the safety officer, who should suggest a way to decrease the risk for workers. However, traditional assessment based on human evaluation and the need for expertise and accuracy in risk assessment have produced muscle injuries. Thus, using artificial intelligence (AI) technology to mitigate risk assessment is cost-effective and accurate. This study proposed a risk assessment system for muscle injuries (RASMI) with AI technology to assess electric welder postures with rapid entire body assessment (REBA) standards to identify the cause of muscle injuries and to warn electric welders when their pose may be a risk. The findings showed that the system can effectively and precisely evaluate the risk assessment of electric welders’ muscle injuries. Additional results showed that they perceive using AI technology to enhance wellness positively in terms of working with warnings for posture adjustment or behavior that can significantly affect an operator’s long-term health and well-being.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Sasithorn Chookaew, Chayapol Ruengdech, Suppachai Howimanporn, Assistant Professor Dr. Thanasan Intarakumthornchai
This work is licensed under a Creative Commons Attribution 4.0 International License.
The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution Licence (CC-BY What does this mean?). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.
By submitting an article the author grants to this journal the non-exclusive right to publish it. The author retains the copyright and the publishing rights for his article without any restrictions.