Heart Rate Variability Based Assessment of Cognitive Workload in Smart Operators

• Autorzy: Salvatore Digiesi , Vito Modesto Manghisi , Francesco Facchini , Mario Massimo Foglia , Elisa Maria Klose , Carlotta Mummolo
• Języki publikacji: EN

Abstrakty

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, sub jective and physiological measures are adopted to measure the work load. The former refers to NASA-TLX test expressing sub jective perceived work load. The latter adopts Heart Rate Variability (HRV) of individuals as an ob jective 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 sub jective and physiological measures adopted. Research findings pave the way for future developments.(original abstract)

Słowa kluczowe

EN

Industry 4.0; Information theory; Measurement

PL

Przemysł 4.0; Teoria informacji; Pomiary

• Czasopismo: Management and Production Engineering Review
• Rocznik: 2020
• Tom: 11
• Numer: nr 3
• Strony: 56--64

Twórcy

autor

Salvatore Digiesi

• Polytechnic University of Bari, Italy

autor

Vito Modesto Manghisi

• Polytechnic University of Bari, Italy

autor

Francesco Facchini

• Polytechnic University of Bari, Italy

autor

Mario Massimo Foglia

• Polytechnic University of Bari, Italy

autor

Elisa Maria Klose

• University of Kassel, Germany

autor

Carlotta Mummolo

• New Jersey Institute of Technology, Newark NJ USA

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Identyfikatory

DOI

10.24425/mper.2020.134932