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| A Neural Network Based Diagnostic System for Classification of Industrial Carrying Jobs With Respect of Low and High Musculoskeletal Injury Risk
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Full
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Source |
International Journal of Biometrics and Bioinformatics (IJBB) |
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Table of Contents |
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Complete Issue PDF(1.63MB) |
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Volume: 6 Issue: 1 |
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Pages: |
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Publication
Date: February 2012 |
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ISSN
(Online): 1985-2347 |
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Pages |
24 - 37 |
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Author(s) |
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Published
Date |
21-02-2012 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
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KEYWORDS: Musculoskeletal Injuries, Physiological Risk, Artificial Neural Network |
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| Even with many years of research efforts, the occupational exposure limits of different risk factors for development of Musculoskeletal disorders (MSDs) have not yet been established. One of the main problems in setting such guidelines is the limited understanding of how different risk factors of MSDs interact in causing the injury, as the nature and mechanism of these disorders are relatively unknown phenomena. The task of an industrial ergonomist is complicated because the potential risk factors that may contribute to the onset of the MSDs interact in a complex way, and require an analyst to apply elaborate data measurement and collection techniques for a realistic job analysis. This makes it difficult to discriminate well between the jobs that place workers at high or low risk of above disorders. The main objective of this study was to to develop an artificial neural network based diagnostic system which can classify industrial jobs according to the potential risk for physiological stressors due to workplace design. Such a system could be useful in hazard analysis and injury prevention due to manual handling of loads in industrial environments. The results showed that the developed diagnostic system can successfully classify jobs into low and high risk categories of above musculoskeletal disorders based on carrying task characteristics. The Neural network based system developed gave the correct classification of the analysed industrial jobs with low and high risk. So, the system can be used as an expert system which, when properly trained, will classify carrying load by male and female workers into two categories of low risk and high risk work, based on the available characteristics factors. |
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| Rohit Sharma : Colleagues
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| Ranjit Singh : Colleagues
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