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 English |
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INTI International University
2023
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| Online Access: | http://eprints.intimal.edu.my/1898/ http://eprints.intimal.edu.my/1898/1/joit2023_28r.pdf http://eprints.intimal.edu.my/1898/2/336 |
| _version_ | 1848766865534353408 |
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| author | Goh, Ching Pang |
| author_facet | Goh, Ching Pang |
| author_sort | Goh, Ching Pang |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:57:56Z |
| format | Article |
| id | intimal-1898 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:57:56Z |
| publishDate | 2023 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-18982025-07-21T09:13:50Z http://eprints.intimal.edu.my/1898/ Detection of Workers’ Behaviour in the Manufacturing Plant using Deep Learning Goh, Ching Pang TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TS Manufactures 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. INTI International University 2023-12 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/1898/1/joit2023_28r.pdf text en cc_by_4 http://eprints.intimal.edu.my/1898/2/336 Goh, Ching Pang (2023) Detection of Workers’ Behaviour in the Manufacturing Plant using Deep Learning. Journal of Innovation and Technology, 2023 (28). pp. 1-7. ISSN 2805-5179 http://ipublishing.intimal.edu.my/joint.html |
| spellingShingle | TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TS Manufactures Goh, Ching Pang Detection of Workers’ Behaviour in the Manufacturing Plant using Deep Learning |
| title | Detection of Workers’ Behaviour in the Manufacturing Plant using Deep
Learning |
| title_full | Detection of Workers’ Behaviour in the Manufacturing Plant using Deep
Learning |
| title_fullStr | Detection of Workers’ Behaviour in the Manufacturing Plant using Deep
Learning |
| title_full_unstemmed | Detection of Workers’ Behaviour in the Manufacturing Plant using Deep
Learning |
| title_short | Detection of Workers’ Behaviour in the Manufacturing Plant using Deep
Learning |
| title_sort | detection of workers’ behaviour in the manufacturing plant using deep
learning |
| topic | TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TS Manufactures |
| url | http://eprints.intimal.edu.my/1898/ http://eprints.intimal.edu.my/1898/ http://eprints.intimal.edu.my/1898/1/joit2023_28r.pdf http://eprints.intimal.edu.my/1898/2/336 |