Abstract
Mental workload remains an essential but challenging aspect of human factors, while machine learning serves as an emerging and expanding research realm to a wide variety of applications. This paper aims to comprehensively bridge the two areas by comparing present state-of-the-art machine learning approaches that are currently utilized for assessing cognitive workload, primarily artificial neural networks and support vector machines. To address and evaluate both approaches, we obtain a physiological data set used to study fear conditioning and cognitive load and format the data to focus primarily on the latter. Ultimately, the results indicate that both techniques can effectively model the data with up to 99% accuracy. Furthermore, under optimal parameter selection, the neural network model produces the highest possible accuracy under a comfortable level of deep learning while the support vector machine model employs greater speed and efficiency while still enjoying a respectably high level 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 and his colleagues Dr. Scott Galster and Mr. Justin Estepp for their 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., Nittala, S., Devabhaktuni, V. (2018). Fundamental Cognitive Workload Assessment: A Machine Learning Comparative Approach. In: Baldwin, C. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2017. Advances in Intelligent Systems and Computing, vol 586. Springer, Cham. https://doi.org/10.1007/978-3-319-60642-2_26
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DOI: https://doi.org/10.1007/978-3-319-60642-2_26
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