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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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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