A Systematic Review of Physiological Measures of Mental Workload

Da Tao, Haibo Tan, Hailiang Wang, Xu Zhang, Xingda Qu and Tingru Zhang
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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
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
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
Tingru Zhang: State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Co., Ltd., Shenzhen 518172, China

IJERPH, 2019, vol. 16, issue 15, 1-23

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.

Keywords: physiological measure; mental workload; human-machine system; systematic review (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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