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2020 | 11 | nr 3 | 56--64
Tytuł artykułu

Heart Rate Variability Based Assessment of Cognitive Workload in Smart Operators

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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)
Opis fizyczny
  • Polytechnic University of Bari, Italy
  • Polytechnic University of Bari, Italy
  • Polytechnic University of Bari, Italy
  • Polytechnic University of Bari, Italy
  • University of Kassel, Germany
  • New Jersey Institute of Technology, Newark NJ USA
  • Lu Y., Industry 4.0: A survey on technologies, applications and open research issues, Journal of Industrial Information Integration, 1, 1-10, 2017.
  • Longo F., Nicoletti L., Padovano A., Smart operators in industry 4.0: A human-centered approach to enhance operators' capabilities and competencies within the new smart factory context, Comput. Ind. Eng., 113, 144-159, 2017.
  • Bauer H., Baur C., Mohr D., Tschiesner A., Weskamp T., Industry 4.0 after the initial hype - where manufacturers are finding value and how they can best capture it, McKinsey Co., 2016.
  • Romero D., Bernus P., Noran O., Stahre J., Berglund A.F., The operator 4.0: Human cyberphysical systems & adaptive automation towards human-automation symbiosis work systems, [in:] Naas I. et al. [Eds], Advances in Production Management Systems. Initiatives for a Sustainable World, APMS 2016, IFIP Advances in Information and Communication Technology, Springer, Cham, vol. 488, 2016.
  • Rose D.M., Gordon R., Age-related cognitive changes and distributed leadership, J. Manag. Dev., 34, 3, 330-339, 2015.
  • Fan X., Zhao C., Hu H., Jiang Y., Review of the evaluation methods of mental workload, Springer International Publishing, vol. 967, 2020.
  • Mackworth J.F., Paced memorizing in a continuous task, J. Exp. Psychol., 58, 3, 206-211, 1959.
  • Kirchner W.K., Age differences in short-term retention of rapidly changing information, J. Exp. Psychol., 55, 4, 352-358, 1958.
  • Zualch G. et al., Influence ofmental workload on job performance, Elektrotechnik und Informationstechnik, 131, 7, 207-211, 2015.
  • Seker A., Using outputs of NASA-TLX for building a mental workload expert system, Gazi Univ. J. Sci., 27, 4, 1131-1142, 2014.
  • Charles R.L., Nixon J., Measuring mental workload using physiological measures: A systematic review, Applied Ergonomics, 74, 221-232, 2019.
  • Bommer S.C., Fendley M., A theoretical framework for evaluating mental workload resources in human systems design for manufacturing operations, Int. J. Ind. Ergon., 63, 7-17, 2018.
  • Mansikka H., Virtanen K., Harris D., Comparison of NASA-TLX scale, modified Cooper-Harper scale and mean inter-beat interval as measures of pilot mental workload during simulated flight tasks, Ergonomics, 62, 2, 246-254, 2019.
  • Luque-Casado A., Perales J.C., Cardenas D., Sanabria D., Heart rate variability and cognitive processing: The autonomic response to task demands, Biol. Psychol., 113, 83-90, 2016.
  • Rusnock C.F., Borghetti B.J., Workload profiles: A continuous measure of mental workload, Int. J. Ind. Ergon., 63, 49-64, 2018.
  • Morton J. et al., Identifying predictive EEG features for cognitive overload detection in assembly workers in Industry 4.0, Proceedings of the 3rd Int. Symp. Hum. Ment. Workload Model. Appl. (H- WORKLOAD 2019), 2019.
  • Coulacoglou C., Saklofske D.H., Executive Function, Theory of Mind, and Adaptive Behavior, [in:] Coulacoglou C., Saklofske D.H., Psychometrics and Psychological Assessment, Academic Press, Else- vier, UK, Chapter V, 89-130, 2017.
  • Bi S., Salvendy G., Analytical modeling and experimental study of human workload in scheduling of advanced manufacturing systems, Int. J. Hum. factors Manuf., 4, 2, 205-234,1994.
  • Bi S., Salvendy G., A proposed methodology for the prediction of mental workload, based on engineering system parameters, Work Stress, 8, 4, 355-371, 1994.
  • Batista D., da Silva H.P., Fred A., Moreira C., Reis M., Ferreira H., Benchmarking of the BITalino Biomedical Toolkit Against an Established Gold Standard, IET Healthcare Technology Letters, 6, 2, 32-36, 2019.
  • McCraty R., Shaffer F., Heart rate variability: New perspectives on physiological mechanisms, assessment of self-regulatory capacity, and health risk, Global Advances In Health and Medicine, 4, 1, 4661, 2015.
  • Wilson G.F., An analysis of mental workload in pilots during flight using multiple psychophysiological measures, International Journal of Aviation Psychology, 12, 1, 3-18, 2002.
  • Landi C.T., Villani V., Ferraguti F., Sabattini L., Secchi C., Fantuzzi C., Relieving operators' workload: Towards affective robotics in industrial scenarios, Mechatronics, 54, 144-154, 2018.
  • Shaffer F., Ginsberg J.P, An overview of heart rate variability metrics and norms, Front. Public Heal., 2017.
  • Pichot V., Roche F., Celle S., Barthelemy J.C., Chouchou F., HRV analysis: A free software for analyzing cardiac autonomic activity, Front. Physiol., 7, 557, 2016.
  • Karavidas M.K., Lehrer P.M., Lu S.E., Vaschillo E., Vaschillo B., Cheng A., The effects of workload on respiratory variables in simulated flight: A preliminary study, Biol. Psychol., 84, 1, 157-160, 2010.
  • Ma J. et al. , Workload influence on fatigue related psychological and physiological performance changes of aviators, PLoS One, 9, 2, e87121, 2014.
  • Zheng B. et al., Workload assessment of surgeons: correlation between NASA TLX and blinks, Surg. Endosc., 26, 10, 2746-2750, 2012.
  • Muth E.R., Moss J.D., Rosopa P.J., Salley J.N., Walker A.D., Respiratory sinus arrhythmia as a measure of cognitive workload, Int. J. Psychophysiol., 83, 1, 96-101, 2012.
  • Byers J.C., Bittner A.C., Hill S.G., Traditional and raw task load index (TLX) correlations: Are paired comparisons necessary?, [in:] Mital A. [Ed.], Advances in industrial ergonomics & safety, Taylor & Francis, London, UK, 1, 481-485, 1989.
  • Moroney W.F., Biers D.W., Eggemeier F.T., Mitchell J.A., A comparison of two scoring procedures with the NASA Task Load Index in a simulated flight task, Proceedings of the IEEE 1992 National Aerospace and Electronics Conference, NAECON 1992, 2, 734-740, 1992
  • Accot J., Zhai S., Beyond Fitts' Law: Models for Trajectory-Based HCI Tasks, proceedings of the ACM SIGCHI Conference on Human factors in computing systems, March 1997, 295-302, 1997.
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