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Easily Applicable Methods for Measuring the Mental Load on Tractor Operators.

Hans Arvidsson, Gunnar Larsson, Anders Larsolle

    Journal of Agricultural Safety and Health
    |May 21, 2020
    PubMed
    Summary

    This study evaluated methods for measuring operator mental load in automated agriculture. Results indicate that a simple assessment protocol effectively measured mental load, unlike heart rate or EEG methods.

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    Area of Science:

    • Agricultural Engineering
    • Human Factors Engineering
    • Cognitive Ergonomics

    Background:

    • Automation in agriculture is shifting operators to supervisory roles.
    • This transition may increase operator stress and mental load, posing risks.
    • Objective mental load measurement is crucial for operator well-being and safety.

    Purpose of the Study:

    • To investigate easily applicable equipment for measuring operator mental load.
    • To compare the effectiveness of heart rate monitoring, electroencephalograph (EEG) evaluation, and an assessment protocol.
    • To identify a reliable tool for assessing mental load in agricultural machinery operators.

    Main Methods:

    • Utilized a driving simulator with exercises (general driving, slalom, loading) and a counting task to induce varying mental loads.
    Keywords:
    AgricultureAwarenessContinuous measurementEEGHeart rateMental loadSimulatorSingle-scale assessmentTractor operatorWork machinery

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  • Measured mental load using three methods: heart rate monitoring, two types of EEG, and a single-scale assessment protocol.
  • Employed a driving simulator to create controlled conditions for mental load assessment.

    • Main Results:

    • The assessment protocol provided clear and reliable data for mental load.
    • Heart rate monitoring did not yield clear results for mental load assessment.
    • Electroencephalograph (EEG) measurements also failed to provide distinct data for mental load.

    Conclusions:

    • A simple assessment protocol is a viable and effective tool for measuring operator mental load.
    • Heart rate and EEG methods were not suitable for assessing mental load in this context.
    • Future research should focus on refining and validating assessment protocols for agricultural automation.