A comparative neural networks and neuro-fuzzy based REBA methodology in ergonomic risk assessment: An application for service workers

Bahar Yalcin Kavus*, P. Gülüm Taş, Alev Taskin

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
25 Downloads (Pure)

Abstract

Non-ergonomic working conditions are the leading causes of musculoskeletal disorders that seriously affect human health. REBA is widely used tool due to its convenience and consideration of all body parts. However, it heavily relies on the subjective judgments of the assessor, leading to inconsistencies in results, and lacks sensitivity in detecting small changes in ergonomic risk factors. Therefore, there is a need to improve the REBA method by integrating it with new technologies. While a few studies have proposed integrating ergonomic risk measurement tools with ANNs, there is a research gap in comparing different types of neural networks and membership functions to determine the most effective approach for improving the performance of REBA. Additionally, there is a need to apply these integrations to real-life case studies to demonstrate their effectiveness in practice. This study proposes a comparative neural network and neuro-fuzzy-based REBA method that includes various types of neural networks and membership functions. The proposed method is applied to service employee who have experienced increased workloads due to the Covid-19 pandemic. The results show that the neuro-fuzzy method is more accurate than the REBA and provides greater flexibility in defining which member belongs to which risk level cluster. This study is critical because it addresses research gaps in integrating neural networks and REBA and applies these integrations to a real-life case study. By comparing different types of neural networks and membership functions, the study provides insights into which approaches are most effective for improving the performance of REBA.
Original languageEnglish
Article number106373
JournalEngineering Applications of Artificial Intelligence
Volume123
DOIs
Publication statusPublished - 2023

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Artificial neural networks
  • Ergonomic
  • Rapid Entire Body Assessment
  • Service employees

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