A Systematic Review of Physiological Measures of Mental Workload.

Da Tao,Hailiang Wang,Xu Zhang,Haibo Tan,Tingru Zhang,Xingda Qu

doi:10.3390/ijerph16152716

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.

Highlights

Mental workload (MWL) has long been cited as an important factor that influences user performance [1,2], and is widely applied in the design and evaluation of complex human-machine systems, such as nuclear power plants [3], cockpits [4], and driving systems [5]
While a number of physiological measures are available for MWL assessment in varied human-computer interaction scenarios, their wide application may be largely inhibited by limited knowledge on their validity to act as effective agents of MWL
The use of physiological measures and their validity for MWL assessment varied across different research domains

Summary

Introduction

Mental workload (MWL) has long been cited as an important factor that influences user performance [1,2], and is widely applied in the design and evaluation of complex human-machine systems, such as nuclear power plants [3], cockpits [4], and driving systems [5]. It has drawn increasing attention over the past two decades, as the increasing application of modern, complex technologies imposes ever greater cognitive demands on operators in varied occupational conditions [2,6]. Among a number of proposed definitions for MWL, Young and Stanton’s definition is a

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