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A Systematic Review of Physiological Measures of Mental Workload
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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6696017/ https://www.ncbi.nlm.nih.gov/pubmed/31366058 http://dx.doi.org/10.3390/ijerph16152716 |
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author | Tao, Da Tan, Haibo Wang, Hailiang Zhang, Xu Qu, Xingda Zhang, Tingru |
author_facet | Tao, Da Tan, Haibo Wang, Hailiang Zhang, Xu Qu, Xingda Zhang, Tingru |
author_sort | Tao, Da |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6696017 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-66960172019-09-05 A Systematic Review of Physiological Measures of Mental Workload Tao, Da Tan, Haibo Wang, Hailiang Zhang, Xu Qu, Xingda Zhang, Tingru Int J Environ Res Public Health Review 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. MDPI 2019-07-30 2019-08 /pmc/articles/PMC6696017/ /pubmed/31366058 http://dx.doi.org/10.3390/ijerph16152716 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Tao, Da Tan, Haibo Wang, Hailiang Zhang, Xu Qu, Xingda Zhang, Tingru A Systematic Review of Physiological Measures of Mental Workload |
title | A Systematic Review of Physiological Measures of Mental Workload |
title_full | A Systematic Review of Physiological Measures of Mental Workload |
title_fullStr | A Systematic Review of Physiological Measures of Mental Workload |
title_full_unstemmed | A Systematic Review of Physiological Measures of Mental Workload |
title_short | A Systematic Review of Physiological Measures of Mental Workload |
title_sort | systematic review of physiological measures of mental workload |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6696017/ https://www.ncbi.nlm.nih.gov/pubmed/31366058 http://dx.doi.org/10.3390/ijerph16152716 |
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