Abstract
Cognitive workload serves as a vital component in many human factors applications. Furthermore, the ability to make assessments, classifications, and predictions of mental load is a well-established yet ongoing research challenge. A wide arsenal of machine learning mechanisms has become available that address cognitive workload assessment, such as support vector machines and artificial neural networks. Due to the longevity of and continuing interest in the latter technique, this paper focuses on neural networks, diving into the many intricate variables and parameters that can make or break an effective neural network model in this area. To evaluate and compare these approaches, we obtain two distinct physiological datasets. Overall, the results indicate that under both datasets, the quasi-Newton optimizer contains a slight edge in accuracy, while stochastic gradient descent is more computationally efficient. Under the second and larger dataset, however, an unsupervised model boasts significantly lower computational runtime while maintaining similar levels of accuracy.
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References
Elkin, C., Nittala, S., Devabhaktuni, V.: Fundamental cognitive workload assessment: a machine learning comparative approach. In: 8th International Conference on Applied Human Factors and Ergonomics (AHFE), pp. 275–284. Springer, Cham (2017)
Berka, C., et al.: EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviat. Space Environ. Med. 78(5), B231–B234 (2007)
Durantin, G., Gagnon, J.-F., Tremblay, S., Dehais, F.: Using near infrared spectroscopy and heart rate variability to detect mental overload. Behav. Brian Search 259, 16–23 (2014)
Niaz, M., Logie, R.H.: Working memory, mental capacity and science education: towards an understanding of the ‘working memory overload hypothesis’. Oxford Rev. Educ. 19(4), 511–525 (1993)
Wilson, G.F., Russell, C.A.: Real-time assessment of mental workload using psychophysiological measures and artificial neural networks. Hum. Factors 45(4), 635–644 (2016)
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(1), 48–56 (2012)
Chatterji, G.B., Sridhar, B.: Neural network based air traffic controller workload prediction. In: Proceedings of the 1999 American Control Conference, pp. 2620–2624 (1999)
Jin, L., et al.: Driver cognitive distraction detection using driving performance measures. Discrete Dyn. Nat. Soc. (2012)
Son, J., Oh, H., Park, M.: Identification of driver cognitive workload using support vector machines with driving performance, physiology and eye movement in a driving simulator. Int. J. Precis. Eng. Manuf. 14(8), 1321–1327 (2013)
Putze, F., Jarvis, J., Schultz, T.: Multimodal recognition of cognitive workload for multitasking in the car. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 3748–3751. IEEE Press, New York (2010)
Liang, Y., Reyes, M., Lee, J.: Real-time detection of driver cognitive distraction using support vector machines. IEEE Trans. Intell. Transp. Syst. 8(2), 340–350 (2007)
Ziegler, M., et al.: Sensing and assessing cognitive workload across multiple tasks. In: Foundations of Augmented Cognition: Neuroergonomics and Operational Neuroscience, pp. 440–450. Springer, Cham (2016)
Yin, Z., Zhang, J.: Identification of temporal variations in mental workload using locally-linear-embedding-based EEG feature reduction and support-vector-machine-based clustering and classification techniques. Comput. Methods Programs Biomed. 115(3), 119–134 (2014)
Solovey, E., et al.: Classifying driver workload using physiological and driving performance data: two field studies. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2014, pp. 4057–4066. ACM, New York (2014)
Calibo, T.K., Blanco, J.A., Firebaugh, S.L.: Cognitive stress recognition. In: 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp. 1471–1475. IEEE Press, New York (2013)
Girouard, A.: Distinguishing difficulty levels with non-invasive brain activity measurements. In: Human-Computer Interaction – INTERACT 2009, pp. 440–452. Springer, Berlin (2009)
Natarajan, A., Xu, K.S., Eriksson, B.: Detecting divisions of the autonomic nervous system using wearables. In: IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), pp. 5761–5764. IEEE Press, New York (2016)
Hefron, R.G., et al.: Deep long short-term memory structures model temporal dependencies improving cognitive workload estimation. Pattern Recogn. Lett. 94, 96–104 (2017)
Gianazza, D.: Analysis of a workload model learned from past sector operations. In: 7th SESAR Innovation Days, SID 2017, pp. 1–9 (2017)
Juhaniak, T., et al.: Pupillary response: removing screen luminosity effects for clearer implicit feedback. In: UMAP (Extended Proceedings) (2016)
Tran, C., Abraham, A., Jain, L.: Decision support systems using hybrid neurocomputing. Neurocomputing 61, 85–97 (2004)
Malsburg, C.: Frank Rosenblatt: principles of neurodynamics: perceptrons and the theory of brain mechanisms. In: Brain Theory, pp. 245–248. Springer, Berlin (1986)
Salakhutdinov, R., Mnih, A., Hinton, G.: Restricted Boltzmann machines for collaborative filtering. In: Proceedings of the 24th International Conference on Machine Learning, ICML 2007, pp. 791–798. ACM, New York (2007)
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Lichman, M.: {UCI} Machine Learning Repository. University of California, Irvine, School of Information and Computer Sciences. http://archive.ics.uci.edu/ml
Acknowledgments
This research is supported by the Dayton Area Graduate Studies Institute (DAGSI) fellowship program for a project titled “Assessment of Team Dynamics Using Adaptive Modeling of Biometric Data.” The authors wish to thank their DAGSI sponsor Dr. Gregory Funke for his continued guidance and support throughout the project. The authors also thank the EECS Department at the University of Toledo for partial support through assistantships and tuition waivers.
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Elkin, C., Devabhaktuni, V. (2019). Analysis of Alternatives for Neural Network Training Techniques in Assessing Cognitive Workload. In: Ayaz, H., Mazur, L. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2018. Advances in Intelligent Systems and Computing, vol 775. Springer, Cham. https://doi.org/10.1007/978-3-319-94866-9_3
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DOI: https://doi.org/10.1007/978-3-319-94866-9_3
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