Human-based dynamics of mental workload in complicated systems

Authors

  • Mohammad-Javad Jafari Department of Occupational Health and Safety Engineering, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran. E-mail: m_jafari@sbmu.ac.ir
  • Farid Zaeri Proteomics Research Center and Department of Biostatistics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. E-mail: fzayeri@gmail.com
  • Amir H. Jafari Medical Physics & Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran. E-mail: h_jafari@tums.ac.ir
  • Amir T. Payandeh Najafabadi Department of Actuarial Science, Faculty of Mathematical Sciences, Shahid Beheshti University, G.C. Evin, 1983963113. E-mail: amirtpayandeh@gmail.com
  • Narmin Hassanzadeh Department of Occupational Health and Safety Engineering, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran. E-mail: narminhassanzadeh@sbmu.ac.ir

DOI:

https://doi.org/10.17179/excli2019-1372

Keywords:

mental workload, ergonomics, archetype, review, system dynamics

Abstract

As a dynamic system in which different factors affect human performance via dynamic interactions, mental workload needs a dynamic measure to monitor its factors and evidence in a complicated system, an approach that is lacking in the literature. The present study introduces a system dynamics-based model for designing feedback mechanisms related to the mental workload through literature review and content analysis of the previous studies. A human-based archetype of mental workload was detected from the data collection process. The archetype is presented at various stages, including dynamic theory, behavior over time, leverage points and model verification. The real validation of the dynamic model was confirmed in an urban train simulator. The dynamic model can be used to analyze the long-term behavior of the mental workload. Decision-makers can benefit from the developed archetypes in evaluating the dynamic impact of their decisions on accident prevention in the complicated systems.

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Published

2019-07-11

How to Cite

Jafari, M.-J., Zaeri, F., Jafari, A. H., Payandeh Najafabadi, A. T., & Hassanzadeh, N. (2019). Human-based dynamics of mental workload in complicated systems. EXCLI Journal, 18, 501–512. https://doi.org/10.17179/excli2019-1372

Issue

Vol. 18 (2019)

Section

Original articles

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