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

Creative Commons License

Yalçın Kavuş B., Gülüm Taş P., Taşkın A.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, cilt.123, sa.Part B, ss.1-17, 2023 (SCI-Expanded)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 123 Sayı: Part B
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1016/j.engappai.2023.106373
  • Dergi Adı: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1-17
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

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.