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Experimental Evaluation of Heart-Based Workload Measures as Related to Their Suitability for Real-Time Applications

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Adaptive Instructional Systems (HCII 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12214))

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Abstract

We conducted an experiment to evaluate the viability of using heart rate parameters for real-time adaptation of applications to the mental state of human operators. The experiment consisted of a fast-jet flying task with secondary tasks in our simulator. We created five mission segments to induce differing levels of workload. During the experiment, heart rate data and subjective workload ratings were collected. The subjective workload ratings show different workload levels for each mission segment. However, from the considered heart rate parameters, we were only able to reproduce two of the known correlations from the literature; namely, average heart rate and high frequency activity of the heart rate variability. Additionally, we encountered the opposite of the expected relationships for the RMSSD of the heart rate as well as the standard deviation across the principal axis of the Poincaré plot. We suppose that the short time-frame, which we deemed necessary for real-time applications, is a possible explanation for our surprising results. Finally, we conclude that heart rate variability parameters may not be robust enough for real-time applications, especially as each measured parameter had participants who showed converse reactions to the average.

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Authors and Affiliations

  1. University of the Bundeswehr, Werner-Heisenberg-Weg 39, 85579, Neubiberg, Germany

    Dennis Mund & Axel Schulte

Authors
  1. Dennis Mund
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  2. Axel Schulte
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Corresponding author

Correspondence to Dennis Mund .

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Editors and Affiliations

  1. Soar Technology, Inc., Orlando, FL, USA

    Robert A. Sottilare

  2. Fraunhofer FKIE, Wachtberg, Germany

    Jessica Schwarz

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Mund, D., Schulte, A. (2020). Experimental Evaluation of Heart-Based Workload Measures as Related to Their Suitability for Real-Time Applications. In: Sottilare, R.A., Schwarz, J. (eds) Adaptive Instructional Systems. HCII 2020. Lecture Notes in Computer Science(), vol 12214. Springer, Cham. https://doi.org/10.1007/978-3-030-50788-6_27

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  • DOI: https://doi.org/10.1007/978-3-030-50788-6_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50787-9

  • Online ISBN: 978-3-030-50788-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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