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A Systematic Review of Physiological Measures of Mental Workload

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  • Da Tao

    (State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Co., Ltd., Shenzhen 518172, China
    Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China)

  • Haibo Tan

    (State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Co., Ltd., Shenzhen 518172, China)

  • Hailiang Wang

    (Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China
    Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen University, Shenzhen 518060, China)

  • Xu Zhang

    (Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China)

  • Xingda Qu

    (State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Co., Ltd., Shenzhen 518172, China
    Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China)

  • Tingru Zhang

    (State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Co., Ltd., Shenzhen 518172, China
    Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China)

Abstract

Mental workload (MWL) can affect human performance and is considered critical in the design and evaluation of complex human-machine systems. While numerous physiological measures are used to assess MWL, there appears no consensus on their validity as effective agents of MWL. This study was conducted to provide a comprehensive understanding of the use of physiological measures of MWL and to synthesize empirical evidence on the validity of the measures to discriminate changes in MWL. A systematical literature search was conducted with four electronic databases for empirical studies measuring MWL with physiological measures. Ninety-one studies were included for analysis. We identified 78 physiological measures, which were distributed in cardiovascular, eye movement, electroencephalogram (EEG), respiration, electromyogram (EMG) and skin categories. Cardiovascular, eye movement and EEG measures were the most widely used across varied research domains, with 76%, 66%, and 71% of times reported a significant association with MWL, respectively. While most physiological measures were found to be able to discriminate changes in MWL, they were not universally valid in all task scenarios. The use of physiological measures and their validity for MWL assessment also varied across different research domains. Our study offers insights into the understanding and selection of appropriate physiological measures for MWL assessment in varied human-machine systems.

Suggested Citation

  • Da Tao & Haibo Tan & Hailiang Wang & Xu Zhang & Xingda Qu & Tingru Zhang, 2019. "A Systematic Review of Physiological Measures of Mental Workload," IJERPH, MDPI, vol. 16(15), pages 1-23, July.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:15:p:2716-:d:252921
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    References listed on IDEAS

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    1. David Moher & Alessandro Liberati & Jennifer Tetzlaff & Douglas G Altman & The PRISMA Group, 2009. "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-6, July.
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    1. Quan Shao & Kaiyue Jiang & Ruoheng Li, 2024. "A numerical evaluation of real-time workloads for ramp controller through optimization of multi-type feature combinations derived from eye tracker, respiratory, and fatigue patterns," PLOS ONE, Public Library of Science, vol. 19(11), pages 1-30, November.
    2. Bartłomiej Naskrent & Witold Grzywiński & Krzysztof Polowy & Arkadiusz Tomczak & Tomasz Jelonek, 2022. "Eye-Tracking in Assessment of the Mental Workload of Harvester Operators," IJERPH, MDPI, vol. 19(9), pages 1-11, April.
    3. Yurong Fan & Jin Liang & Xiaodong Cao & Liping Pang & Jie Zhang, 2022. "Effects of Noise Exposure and Mental Workload on Physiological Responses during Task Execution," IJERPH, MDPI, vol. 19(19), pages 1-21, September.
    4. Yewei Ouyang & Cheng Cheng & Dan Wang & Shiyi He & Lan Zheng, 2023. "The Design of a Posture Instruction Atlas and the Prevention of Construction Workers’ Work-Related Musculoskeletal Disorders (WMSDs): A Study on Attention Allocation and Cognitive Load Based on Eye Tr," Sustainability, MDPI, vol. 15(19), pages 1-17, September.

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