Skip to main content
SpringerLink
Log in

Adaptive Fuzzy Modeling Based Assessment of Operator Functional State in Complex Human–Machine Systems

  • Chapter
  • First Online:
Complex Systems

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 55))

Abstract

The quantitative analysis of human operator functional state (OFS) plays a crucial role in modeling and adaptive control of a large class of complex and safety-critical human–machine systems arising from such diverse fields as manned aerospace, air traffic control, and nuclear power plant. In this chapter, the OFS is quantitatively estimated using multiple sources of measured psychophysiological data. In the data acquisition experiments, an automation-enhanced cabin air management system (aCAMS) was employed to simulate with high fidelity a highly complex multitask platform of human–machine cooperative process control . Two types of adaptive fuzzy models, i.e., adaptive-network-based fuzzy inference system (ANFIS ) and genetic algorithm (GA)-based Mamdani fuzzy model, are constructed to estimate the temporal fluctuations of the OFS. The fuzzy models are used to reveal the complex unknown correlation between the psychophysiological (i.e., electroencephalographical and cardiovascular) variables and operator performance (i.e., primary-task-related performance). The adaptive fuzzy modeling paradigm was validated using the data measured from 11 young healthy and well-trained male subjects (2 trials for each), who were engaged in the manual control tasks under aCAMS experimental environment. The fuzzy modeling methods proposed may provide an objective and quantitative way to accurately estimate the OFS related to mental or cognitive workload (stress) of the human operator .

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Hancock, P.A., Desmond, P.A. (eds.): Stress, Workload and Fatigue. Erlbaum, Mahwah (2001)

    Google Scholar 

  2. Hockey, G.R.J., Gaillard, A.W.K., Burov, O. (eds.): Operator Functional State: The Assessment and Prediction of Human Performance Degradation in Complex Tasks. IOS, Amsterdam (2003)

    Google Scholar 

  3. Hockey, G.R.J.: Compensatory control in the regulation of human performance under stress and high workload: a cognitive energetical framework. Biol. Psychol. 45, 72–93 (1997)

    Google Scholar 

  4. Nickel P., Roberts A.C., Hockey G.R.J.: Assessment of high risk operator functional state markers in dynamical systems – preliminary results and implications. In Proceeding Human Factors and Ergonomics Society Europe Chapter Annual Meeting 2005, Turin, Italy, 26–28 October 2005

    Google Scholar 

  5. Nickel P., Hockey G.R.J., Roberts A.C., Roberts M.H.: Markers of high risk operator functional state in adaptive control of process automation. In: Proceeding of IEA (2006)

    Google Scholar 

  6. Wilson, G.F., Russell, C.A.: Performance enhancement in an uninhabited air vehicle task using psychophysiologically determined adaptive aiding. Hum. Factors 49, 1005–1018 (2007)

    Article  Google Scholar 

  7. Wilson G.F., Russell C.A.: Operator functional state classification using multiple psychophysiological features in an air traffic control task, Hum. Factors, 45, 381–389 (2003)

    Google Scholar 

  8. Wilson G.F., Russell C.A.: Real-Time assessment of mental workload using psychophysiological measures and artificial neural networks, Hum. Factors, 45, 635–644 (2003)

    Google Scholar 

  9. Cox, E.: Fuzzy fundamentals, IEEE Spectrum, 58–61 (1992)

    Google Scholar 

  10. de Silva, C.W.: Intelligent Control and Fuzzy Logic Applications. CRC Press, Boca Raton (1995)

    MATH  Google Scholar 

  11. Mendel, J.M.: Uncertainty, fuzzy logic and signal processing. Signal Process. 80, 913–933 (2000)

    Article  MATH  Google Scholar 

  12. Parasuraman, R., Masalonis, A.J., Hancock, P.A.: Fuzzy signal detection theory: basic postulates and formulas for analyzing human and machine performance. Hum. Factors 42, 636–659 (2000)

    Article  Google Scholar 

  13. Mahfouf M., El-Samahy E., Linkens D.A.: Development of a grey-box closed-loop model relating to volunteers subjected to physical stress. In: Proceeding 15th IFAC World Congress, Barcelona (2002)

    Google Scholar 

  14. Fraser, W.D., Human Factors of CC-130 Operations: Future Aircraft Performance Measures, Technical Report., R-98–16, Toronto: DCIEM (1998)

    Google Scholar 

  15. Moon, B.S., Lee, H.C., Lee, Y.H., Park, J.C., Oh, I.S., Lee, J.W.: Fuzzy systems to process ECG and EEG signals for quantification of the mental workload. Inf. Sci. 142, 23–35 (2002)

    Article  MATH  Google Scholar 

  16. Hockey G.R.J., Nickel P., Roberts A., Mahfouf M., Linkens D.A.: Implementing adaptive automation using on-line detection of high risk operator functional state. In: Proceeding 9th International Symposium of the ISSA,Nice, France, 1–3, March 2006

    Google Scholar 

  17. Zhang J., Mahfouf M., Linkens D.A. et al.: Adaptive fuzzy model of operator functional state in human-machine system: a preliminary study. In: Proceeding IASTED International. Conference. on Biomedical Engineering, Innsbruck, Austria, 14–16, February 2007

    Google Scholar 

  18. Ntuen C.A., Estimating (human) cognitive states in a human-machine system: a fuzzy modeling approach. In: Proceeding 4th Symposium on Human Interaction with Complex Systems (HICS), p. 209 (1998)

    Google Scholar 

  19. Kaber, D.B., Riley, J.M., Kheng-Wooi, T., Endsley, M.R.: On the design of adaptive automation for complex systems. Int. J. Cogn. Ergon. 5, 37–57 (2001)

    Article  Google Scholar 

  20. Hockey, G.R.J., Wastell, D., Sauer, J.: Effects of sleep deprivation and user interface on complex performance: a multi-level analysis of compensatory control. Hum. Factors 40, 233–253 (1998)

    Article  Google Scholar 

  21. Sauer, J., Wastell, D.G., Hockey, G.R.J.: A conceptual framework for designing micro-worlds for complex work domains: a case study on the Cabin Air Management System. Comput. Hum. Behav. 16, 45–58 (2000)

    Article  Google Scholar 

  22. Lorenz, B., Di Nocera, F., Röttger, S., Parasuraman, R.: Automated fault-management in a simulated spaceflight micro-world. Aviat. Space Environ. Med. 73, 886–897 (2002)

    Google Scholar 

  23. Jasper, H.H.: Report of the committee on methods of clinical examination in electroencephalography. Electroencephalogr. Clin. Neurophysiol. 10, 370–375 (1958)

    Article  Google Scholar 

  24. Luczak, H., Laurig, W.: An analysis of heart rate variability. Ergonomics 16, 85–98 (1973)

    Article  Google Scholar 

  25. Kitney, R.I., Rompelman, O. (eds.): Study of Heart-Rate Variability. Clarendon Press, Oxford (1980)

    Google Scholar 

  26. Boucsein, W., Backs, R.W.: Engineering psychophysiology as a discipline: Historical and theoretical aspects. In: Backs, R.W., Boucsein, W. (eds.) Engineering Psychophysiology: Issues and Applications, pp. 3–30. Erlbaum, Mahwah (2000)

    Google Scholar 

  27. Izsó, L.: Developing Evaluation Methodologies for Human-Computer Interaction. Delft University Press, Delft (2001)

    Google Scholar 

  28. Nickel, P., Nachreiner, F.: Sensitivity and diagnosticity of the 0.1-Hz component of heart rate variability as an indicator of mental workload. Hum. Factors 45, 575–590 (2003)

    Article  Google Scholar 

  29. Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  30. Zadeh, L.A.: An outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man Cybern. 3, 28–44 (1973)

    Article  MathSciNet  MATH  Google Scholar 

  31. Nie, J., Linkens, D.A.: Fuzzy-Neural Control: Principles, Algorithms and Applications. Prentice-Hall, New York (1995)

    MATH  Google Scholar 

  32. Wang, L.X.: A Course on Fuzzy Systems and Control. Prentice-Hall, Englewood Cliffs (1997)

    Google Scholar 

  33. Fahrenberg, J., Wientjes, C.W.J.: Recording methods in applied environments. In: Backs, R.W., Boucsein, W. (eds.) Engineering Psychophysiology: Issues and Applications, pp. 111–136. Erlbaum, Mahwah (2000)

    Google Scholar 

  34. Tattersall, A.J., Hockey, G.R.J.: Level of operator control and changes in heart rate variability during simulated flight maintenance. Hum. Factors 37, 682–698 (1995)

    Article  Google Scholar 

  35. Pope, A.T., Bogart, E.H., Bartolome, D.S.: Biocybernetic system evaluates indices of operator engagement in automated task. Biol. Cybern. 40, 187–195 (1995)

    Google Scholar 

  36. Lorenz, B.: Detection and prediction of an automation-induced state of impaired operator competence. In: Proceeding NATO ARW on Operator Functional State (2002)

    Google Scholar 

  37. Zhang J., Nassef A., Mahfouf M., Linkens D.A. et al.: Modeling and analysis of HRV under physical and mental workloads. In: Proceeding. 6th IFAC Symposium on Modeling and Control in Biomedical Systems, (2006) pp.189–194. Reims, France, 20–22 September 2006

    Google Scholar 

  38. Tagaki, T., Sugeno, M.: Fuzzy identification of systems and its application to modeling and control. IEEE Trans. Syst. Man Cybern. 15, 116–132 (1985)

    Article  Google Scholar 

  39. Jang, J.: ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23, 665–685 (1993)

    Article  Google Scholar 

  40. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley (1989)

    Google Scholar 

  41. Linkens, D.A., Nyongesa, H.O.: Genetic algorithms for fuzzy control (Part 1: Offline system development and application and Part 2: Online system development and application). IEE Proc. Control Theory Appl. 142, 161–185 (1995)

    Article  MATH  Google Scholar 

  42. Harding, R.M.: Human Respiratory Responses During High Performance Flight. AGARD/NATO, AG 312, Neuilly-sur-Seine, France (1987)

    Google Scholar 

  43. Schvaneveldt, R.W., Reid, G.B., Gomez, R.L., Rice, S.: Modeling mental workload. Cogn. Technol. 3, 19–31 (1998)

    Google Scholar 

  44. Parasuraman, R., Sheridan, T.B., Wickens, C.D.: A model for types and levels of human interaction with automation. IEEE Trans. Syst. Man Cybern. 30, 286–297 (2000)

    Article  Google Scholar 

  45. Scerbo, M., Freeman, F.G., Mikulka, P.J., Parasuraman, R., Di Docero, F., Prinzel, L.J.: The Efficacy of Psychophysiological Measures for Implementing Adaptive Control, NASA – Technical Paper (2001-211018). Langley Research Center, Hampton (2001)

    Google Scholar 

  46. Laine T.I., Bauer K.W., Lanning J.J. W., Russell C.A., Wilson G.F.: Selection of input features across subjects for classifying crewmember workload using artificial neural networks, IEEE Trans. Syst. Man Cybern. – Part A: Syst. Hum. 32 691–704 (2002)

    Google Scholar 

  47. Berka, C., Levendowski, D.J., Lumicao, M.N., Yau, A., Davis, G., Zivkovic, V.T., Olmstead, R.E., Tremoulet, P.D., Craven, P.L.: EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviat. Space Environ. Med. 78, B231–B244 (2007)

    Google Scholar 

  48. Di Nocera F., Camilli M., Terenzi M.: A random glance at the flight deck: Pilots’ scanning strategies and the real-time assessment of mental workload, J. Cogn. Eng. Decis. Mak. 1 271–285 (2007)

    Google Scholar 

  49. Parasuraman, R., Mouloua, M., Molloy, R.: Effects of adaptive task allocation on monitoring of automated systems. Hum. Factors 38, 665–679 (1996)

    Article  Google Scholar 

  50. Gevins, A., Smith, M.E., McEvoy, L., Yu, D.: High-resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice. Cereb. Cortex 7, 374–385 (1997)

    Article  Google Scholar 

  51. Smith, M.E., Gevins, A., Brown, H., Karnik, A., Du, R.: Monitoring task loading with multivariate EEG measures during complex forms of human-computer interaction. Hum. Factors 43, 366–380 (2001)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant No. 61075070 and Key Grant No. 11232005. The authors would also like to thank Professor D. Manzey, Technical University of Berlin, Germany for providing the AUTO-CAMS software which was used in the data acquisition experiments. The invitation from Professor G. M. Dimirovski, for the chapter contribution is also gratefully acknowledged.

Author information

Authors and Affiliations

  1. Department of Automation, East China University of Science and Technology, Shanghai, 200237, People’s Republic of China

    Jianhua Zhang

  2. Institute of Cognitive Neurodynamics, East China University of Science and Technology, Shanghai, 200237, People’s Republic of China

    Rubin Wang

Authors
  1. Jianhua Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rubin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianhua Zhang .

Editor information

Editors and Affiliations

  1. School of Electrical Engineering an, St. Cyril and St. Methodius Univers, Skopje, Macedonia

    Georgi M. Dimirovski

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Zhang, J., Wang, R. (2016). Adaptive Fuzzy Modeling Based Assessment of Operator Functional State in Complex Human–Machine Systems. In: Dimirovski, G. (eds) Complex Systems. Studies in Systems, Decision and Control, vol 55. Springer, Cham. https://doi.org/10.1007/978-3-319-28860-4_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-28860-4_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28858-1

  • Online ISBN: 978-3-319-28860-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation