Detection of Workers’ Behaviour in the Manufacturing Plant using Deep Learning
In the modern manufacturing landscape, optimizing productivity is a paramount challenge, particularly in dynamic, non-concentrative environments where human activities are diverse and complex. Accurately monitoring and analyzing worker behavior is crucial for enhancing manufacturing processes,...
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Format: | Article |
Language: | English |
Published: |
INTI International University
2023
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Online Access: | http://eprints.intimal.edu.my/1898/1/joit2023_28.pdf http://eprints.intimal.edu.my/1898/ http://ipublishing.intimal.edu.my/joint.html |
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Summary: | In the modern manufacturing landscape, optimizing productivity is a paramount
challenge, particularly in dynamic, non-concentrative environments where human activities
are diverse and complex. Accurately monitoring and analyzing worker behavior is crucial for
enhancing manufacturing processes, but traditional methods fall short in these settings due to
their reliance on simplistic global image features and manual classification. Addressing this
gap, this paper introduces a groundbreaking vision-based capture technology, integrated into a
manufacturing monitoring system. This technology significantly advances productivity by
providing a nuanced assessment of worker behavior. It departs from conventional approaches
by employing gait recognition techniques, which effectively match input sequences with
predefined models. This method adeptly navigates the hurdles of data scarcity, diverse human
behaviors, and visual variations typical in manufacturing environments. Utilizing machine
learning algorithms, our system learns and detects intricate activities from worker behavior
sequences, offering a sophisticated analysis of worker efficiency. The primary aim is to
quantify human behavior based on learning rates, thereby facilitating improved production
control. Our findings are promising, demonstrating an impressive 99% accuracy in behavior
detection. This high level of precision underscores the potential of our technology to transform
manufacturing productivity and worker monitoring practices. |
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