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Machine Learning Technique for Data Fusion and Cognitive Load Classification Using an Eye Tracker

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Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23) (ACR 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 700))

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Abstract

Advanced Driver Assistance Systems (ADAS) and other human-machine automation systems should be able to accurately recognize and adapt to the cognitive load of the user. For effective human-machine automation, it is important to develop techniques to automatically predict the cognitive load, based on data from non-invasive and low-cost sensors, such as eye-trackers. In this paper, we investigate the use of machine learning (ML) to classify the cognitive load of a participant performing n-back tasks. The ML models are trained using a large number of raw eye-tracking metrics. Our results demonstrate that tree-based algorithms are able to quickly predict cognitive load with a high degree of accuracy compared to other methods, indicating their potential usefulness for real-time applications.

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Authors and Affiliations

  1. University of Windsor, Windsor, ON, N9B 3P4, Canada

    Aaron Collins, Parthana Pillai, Balakumar Balasingam & Arunita Jaekel

Authors
  1. Aaron Collins
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  2. Parthana Pillai
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  3. Balakumar Balasingam
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  4. Arunita Jaekel
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Corresponding author

Correspondence to Arunita Jaekel .

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Editors and Affiliations

  1. University of Detroit Mercy, Detroit, MI, USA

    Kevin Daimi

  2. Charles Sturt University, Sydney, NSW, Australia

    Abeer Al Sadoon

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Collins, A., Pillai, P., Balasingam, B., Jaekel, A. (2023). Machine Learning Technique for Data Fusion and Cognitive Load Classification Using an Eye Tracker. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23). ACR 2023. Lecture Notes in Networks and Systems, vol 700. Springer, Cham. https://doi.org/10.1007/978-3-031-33743-7_7

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