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Measuring mental workload using physiological measures: A systematic review.

Rebecca L Charles1, Jim Nixon1

  • 1Cranfield University, Martell House, Cranfield, Bedford, MK43 0TR, United Kingdom.

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Summary
This summary is machine-generated.

This review synthesizes physiological measures for evaluating mental workload (MWL). While no single measure is perfect, evidence supports using various physiological sensors to assess MWL in different settings.

Keywords:
Mental workloadPhysiological measuresSystematic reviewTaskload

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Area of Science:

  • Human-Computer Interaction
  • Cognitive Engineering
  • Physiological Computing

Background:

  • Technological advancements enable physiological measurements to assess operator states.
  • Mental workload (MWL) is frequently evaluated using diverse physiological sensor data.
  • Existing literature lacks a synthesized guide for selecting appropriate MWL assessment measures.

Purpose of the Study:

  • To systematically review and synthesize literature on physiological measures for MWL evaluation.
  • To provide practitioners with key findings and limitations for selecting MWL assessment methods.
  • To inform the deployment of physiological measures in applied and laboratory settings.

Main Methods:

  • Systematic review of 58 peer-reviewed journal articles presenting original data.
  • Inclusion of physiological measures: electrocardiographic, respiratory, dermal, blood pressure, ocular, and electroencephalographic (when combined).
  • Analysis of applied and experimental studies across various domains, with emphasis on safety-critical applications.

Main Results:

  • Six key physiological measures for MWL assessment were identified and summarized.
  • Evidence-based guidance on deploying each measure and their characteristics affecting research use was provided.
  • Measures demonstrated ability to discriminate MWL differences based on task type, load, and difficulty, with varying sensitivity to taskload changes.

Conclusions:

  • No single physiological measure definitively discriminates mental workload.
  • A growing empirical basis exists to inform both scientific research and practical application of MWL assessment.
  • Selection of appropriate physiological measures depends on specific research or applied contexts and desired sensitivity.