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Heart rate variability based assessment of cognitive workload in smart operators

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EN
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EN
The study on cognitive workload is a field of research of high interest in the digital society. The implementation of ‘Industry 4.0’ paradigm asks the smart operators in the digital factory to accomplish more ‘cognitive-oriented’ than ‘physical-oriented’ tasks. The Authors propose an analytical model in the information theory framework to estimate the cognitive workload of operators. In the model, subjective and physiological measures are adopted to measure the work load. The former refers to NASA-TLX test expressing subjective perceived work load. The latter adopts Heart Rate Variability (HRV) of individuals as an objective indirect measure of the work load. Subjective and physiological measures have been obtained by experiments on a sample subjects. Subjects were asked to accomplish standardized tasks with different cognitive loads according to the ‘n-back’ test procedure defined in literature. Results obtained showed potentialities and limits of the analytical model proposed as well as of the experimental subjective and physiological measures adopted. Research findings pave the way for future developments.
Twórcy
  • Polytechnic University of Bari, Department of Mechanics, Mathematics and Management, Italy
  • Polytechnic University of Bari, Department of Mechanics, Mathematics and Management, Italy
  • Department of Mechanics, Mathematics and Management, Polytechnic University of Bari, Via Orabona 4, Bari, Italy
  • University of Kassel, FG Industrial and Organizational Psychology, Germany
  • Polytechnic University of Bari, Department of Mechanics, Mathematics and Management, Italy
  • Department of Biomedical Engineering, New Jersey Institute of Technology, Newark NJ USA
Bibliografia
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  • [6] Fan X., Zhao C., Hu H., Jiang Y., Review of the evaluation methods of mental workload, Springer International Publishing, vol. 967, 2020.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5e2cc207-aaa1-4e29-b503-2c99e8d3e7bc
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