Abstract
Adaptive systems typically comprise components that interrelate and interact to enable the whole system to respond and adjust to changes in the environment, operator, and task in order to regulate or maintain a level of performance or homeostasis. In so doing, they enable a degree of individualization and customization for many technological innovations such as managing the use of automation. Adaptive systems often involve some kind of feedback or closed-loop which requires a criteria for determining invoking thresholds, as well as some type of classification algorithm that models the type of changes to which the system has to adapt. This paper outlines the issues, considerations, and challenges associated with classification in adaptive systems, and reviews several algorithms that implement the feedback loop in neuro-ergonomic applications. These include logistic regression, Naïve-Bayes, artificial neural networks (ANN), and support vector machines (SVM) techniques.
The original version of this chapter was revised: Incorrect figures have been corrected. The erratum to this chapter is available at https://doi.org/10.1007/978-3-319-94223-0_50
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Change history
30 August 2018
An erratum has been published.
References
Baldwin, C.L., Penaranda, B.N.: Adaptive training using an artificial neural network and EEG metrics for within- and cross-task workload classification. NeuroImage 59, 48–56 (2012). https://doi.org/10.1016/j.neuroimage.2011.07.047
Hannula, M., Huttunen, K., Koskelo, J., Laitinen, T., Leino, T.: Comparison between artificial neural network and multilinear regression models in an evaluation of cognitive workload in a flight simulator. Comput. Biol. Med. 38, 1163–1170 (2008). https://doi.org/10.1016/j.compbiomed.2008.09.007
Prinzel III, L.J., Freeman, F.G., Scerbo, M.W., Mikulka, P.J., Pope, A.T.: Effects of a psychophysiological system for adaptive automation on performance, workload, and the event-related potential P300 component. Hum. Factors 45, 601–614 (2003)
Prinzel, L.J., Freeman, F.G., Scerbo, M.W., Mikulka, P.J., Pope, A.T.: A closed-loop system for examining psychophysiological measures for adaptive task allocation. Int. J. Aviat. Psychol. 10, 393–410 (2000). https://doi.org/10.1207/S15327108IJAP1004_6
Freeman, F.G., Mikulka, P.J., Scerbo, M.W., Prinzel, L.J., Clouatre, K.: Evaluation of a psychophysiologically controlled adaptive automation system, using performance on a tracking task. Appl. Psychophysiol. Biofeedback 25, 103–115 (2000)
Wilson, G.F., Russell, C.A.: Performance enhancement in an uninhabited air vehicle task using psychophysiologically determined adaptive aiding. Hum. Factors Soc. J. Hum. Factors Ergon. 49, 1005–1018 (2007). https://doi.org/10.1518/001872007X249875
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)
Christensen, J.C., Estepp, J.R., Wilson, G.F., Russell, C.A.: The effects of day-to-day variability of physiological data on operator functional state classification. NeuroImage 59, 57–63 (2012). https://doi.org/10.1016/j.neuroimage.2011.07.091
Feigh, K.M., Dorneich, M.C., Hayes, C.C.: Toward a characterization of adaptive systems: a framework for researchers and system designers. Hum. Factors J. Hum. Factors Ergon. Soc. 54, 1008–1024 (2012). https://doi.org/10.1177/0018720812443983
Hockey, G.R.J.: Operator Functional State: The Assessment and Prediction of Human Performance Degradation in Complex Tasks. IOS Press, Amsterdam (2003)
Parasuraman, R., Mouloua, M., Molloy, R.: Effects of adaptive task allocation on monitoring of automated systems. Hum. Factors 38, 665–679 (1996)
Dimensionality Reduction Algorithms: Strengths and Weaknesses. https://elitedatascience.com/dimensionality-reduction-algorithms
Field, A.: Discovering statistics using SPSS. Sage publications, Thousand Oaks (2009)
Goodness-of-fit tests for Binary Logistic Regression. http://support.minitab.com/en-us/minitab-express/1/help-and-how-to/modeling-statistics/regression/how-to/binary-logistic-regression/interpret-the-results/all-statistics-and-graphs/goodness-of-fit-tests/
Mehrotra, K., Mohan, C.K., Ranka, S.: Elements of Artificial Neural Networks (Complex Adaptive Systems). MIT Press, Cambridge, MA (1997)
Ruck, D.W., Rogers, S.K., Kabrisky, M.: Feature selection using a multilayer perceptron. J. Neural Netw. Comput. 2, 40–48 (1990)
Provost, F., Fawcett, T.: Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking. O’Reilly Media Inc, Sebastopol (2013)
Besson, P., Dousset, E., Bourdin, C., Bringoux, L., Marqueste, T., Mestre, D.R., Vercher, J.-L.: Bayesian network classifiers inferring workload from physiological features: compared performance. In: 2012 IEEE Intelligent Vehicles Symposium (IV), pp. 282–287. IEEE (2012)
Johannes, B., Gaillard, A.W.K.: A methodology to compensate for individual differences in psychophysiological assessment. Biol. Psychol. 96, 77–85 (2014). https://doi.org/10.1016/j.biopsycho.2013.11.004
Wang, Z., Hope, R.M., Wang, Z., Ji, Q., Gray, W.D.: Cross-subject workload classification with a hierarchical Bayes model. NeuroImage 59, 64–69 (2012). https://doi.org/10.1016/j.neuroimage.2011.07.094
Understanding Support Vector Machine algorithm from examples (along with code). https://www.analyticsvidhya.com/blog/2017/09/understaing-support-vector-machine-example-code/
Yeo, M.V.M., Li, X., Shen, K., Wilder-Smith, E.P.V.: Can SVM be used for automatic EEG detection of drowsiness during car driving? Saf. Sci. 47, 115–124 (2009). https://doi.org/10.1016/j.ssci.2008.01.007
Acknowledgments
This research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911 NF-14-2-0021. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied of the Army Research Laboratory of or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Teo, G., Reinerman-Jones, L. (2019). Classification Algorithms in Adaptive Systems for Neuro-Ergonomic Applications. In: Cassenti, D. (eds) Advances in Human Factors in Simulation and Modeling. AHFE 2018. Advances in Intelligent Systems and Computing, vol 780. Springer, Cham. https://doi.org/10.1007/978-3-319-94223-0_39
Download citation
DOI: https://doi.org/10.1007/978-3-319-94223-0_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94222-3
Online ISBN: 978-3-319-94223-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)