The Evaluation of Operator's Mental Workload in Operation Control Center Division in the Railway Industry

Livia Putri Sulistiyo, Maya Arlini Puspasari

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The operation control center (OCC) in the railway industry is an automated-based work system controlled by the operators. The mental workload in OCC is necessary to evaluate due to a dynamic workload. The operators' mental workload was classified into mental underload conditions because of a long working hour with simple and repetitive tasks demand. This research was conducted with 17 male operators within two hours of peak sessions in the morning and afternoon shifts. The operators were performed a single and dual-task workload using EEG to find the optimal workload. NASA-TLX was chosen as a subjective assessment to measure mental workload. ANN was used to predict the operator's mental workload. The result shows significant differences between two different workloads and an increase of theta waves in frontal lobes. The ANN result claims 96.65% from the R-square value of the Testing data set were accurate to predict mental workload. The precise high accuracy level means that dual-task can be implemented in the OCC division to improve operators' workload and reduce monotonous activity.

Original languageEnglish
Title of host publicationICIBE 2020 - 2020 6th International Conference on Industrial and Business Engineering
PublisherAssociation for Computing Machinery
Pages202-207
Number of pages6
ISBN (Electronic)9781450387880
DOIs
Publication statusPublished - 27 Sep 2020
Event6th International Conference on Industrial and Business Engineering, ICIBE 2020 - Macau, China
Duration: 27 Sep 202029 Sep 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference6th International Conference on Industrial and Business Engineering, ICIBE 2020
Country/TerritoryChina
CityMacau
Period27/09/2029/09/20

Keywords

  • Artificial neural network
  • EEG
  • Mental workload
  • Monotonous activity
  • NASA-TLX

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