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

Published: 10 December 2020 Publication History

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.

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ICIBE '20: Proceedings of the 6th International Conference on Industrial and Business Engineering
September 2020
235 pages
ISBN:9781450387880
DOI:10.1145/3429551
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 December 2020

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Author Tags

  1. Artificial neural network
  2. EEG
  3. Mental workload
  4. Monotonous activity
  5. NASA-TLX

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