Measuring mental workload using physiological measures: A systematic review.

By Rebecca Charles, Jim Nixon. Applied Ergonomics, 2019

581 citations recorded by the source corpus.

Technological advances have led to physiological measurement being increasingly used to measure and predict operator states. Mental workload (MWL) in particular has been characterised using a variety of physiological sensor data. This systematic review contributes a synthesis of the literature summarising key findings to assist practitioners to select measures for use in evaluation of MWL. We also describe limitations of the methods to assist selection when being deployed in applied or laboratory settings. We detail fifty-eight peer reviewed journal articles which present original data using physiological measures to include electrocardiographic, respiratory, dermal, blood pressure and ocular. Electroencephalographic measures have been included if they are presented with another measure to constrain scope. The literature reviewed covers a range of applied and experimental studies across various domains, safety-critical applications being highly represented in the sample of applied literature reviewed. We present a summary of the six measures and provide an evidence base which includes how to deploy each measure, and characteristics that can affect or preclude the use of a measure in research. Measures can be used to discriminate differences in MWL caused by task type, task load, and in some cases task difficulty. Varying ranges of sensitivity to sudden or gradual changes in taskload are also evident across the six measures. We conclude that there is no single measure that clearly discriminates mental workload but there is a growing empirical basis with which to inform both science and practice.

Subfields: Radiological and Ultrasound Technology, Social Psychology, Surgery

View Paper: https://www.semanticscholar.org/paper/0f132d9e927d9a25b1ebe1bd86d246fe048bf162

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Radiological and Ultrasound Technology

1. Changkun Ou
2. Sven Mayer
3. Francesco Chiossi

In Social Psychology

1. Changkun Ou
2. Sven Mayer
3. Francesco Chiossi

In Surgery

1. Changkun Ou
2. Sven Mayer
3. Francesco Chiossi

Citations in the corpus

• Review of Eye Tracking Metrics Involved in Emotional and Cognitive Processes (2021, background): “However, defining workload can be a rather subjective task depending on how different people with different experience and abilities can handle the same task [140], [141].”
• Exploring Physiological Correlates of Visual Complexity Adaptation: Insights from EDA, ECG, and EEG Data for Adaptation Evaluation in VR Adaptive Systems (2023, methodology): “However, there is no universal physiological measure or method to index mental workload, as different physiological measures have been shown to discriminate between different features of task load [10].”