Measuring mental workload using physiological measures: A systematic review

Abstract

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 state of the art review contributes a synthesis of the literature. We present a systematic review of 58 peer reviewed journal articles which present original data using primarily peripheral nervous system (PNS) measures to include electrocardiographic, blood pressure, respiratory, ocular and dermal sensors. In addition, electroencephalographic measures have been included if they are presented with a PNS measure. The literature reviewed covers a wide range of applied and experimental studies across various domains, with aviation being highly represented in the sample of applied literature reviewed. We present a summary of the six measures and provide a high level evidence base including how to deploy each measure, and characteristics that can affect or preclude a measure from use in a study. Measures can be used to discriminate differences in MWL caused by task type, task load, and in some cases task difficulty. In addition there is varying ranges of sensitivity to sudden or gradual changes in taskload 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.

Subtypes of nurses' mental workload and interaction patterns with fatigue and work engagement during coronavirus disease 2019 (COVID-19) outbreak: A latent class analysis

“…Studies have suggested that a 4-item NASA-TLX may be more suitable for measuring nurse workload, however there is still controversy regarding the application of 4-item or 6-item versions of NASA-TLX in the clinical setting (Galy et al, 2018;Charles et al, 2019). Our current study utilized the 6-item version of NASA-TLX in order to assess different aspects of nurse workload.…”

“…The most widely used instrument for assessment of overall subjective workload is the National Aeronautics and Space Administration Task Load Index (NASA-TLX), which contains a total of six dimensions: mental, physical, and temporal task demands, as well as effort, frustration, and perceived performance (Charles et al, 2019). The NASA-TLX has been validated and widely used as an assessment tool in a healthcare setting.…”

A Cyber-Physical-Human System for One-to-Many UAS Operations: Cognitive Load Analysis

“…The measurement of the physiological response and inferring cognitive states, with and without system adaptation has been demonstrated in previous studies [ 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. However, there are still considerable challenges with the implementation of such methods, where some extensive reviews have identified that measures of MWL are not universally valid for all task scenarios [ 19 , 20 ]. A reason for this is that the physiological responses for MWL can be scenario dependent and are thus influenced by a range of individual differences and task characteristics [ 20 ].…”

Multimodal Study of the Effects of Varying Task Load Utilizing EEG, GSR and Eye-Tracking

“…Measurements usually fall into three broad categories. Behavioral measurements such as various types of eye movement (de Greef et al, 2009) or gross motor behaviors (Boxtel and Jesserun, 1993), subjective measures including self-reporting scales such as the multidimensional SWAT (Reid and Nygren, 1988) and NASA Task Load Index questionnaires (Hart and Staveland, 1988), and objective physiological measurements which capture a signal that potentially scales or correlates with task loading (Chen et al, 2012;Lean and Shan, 2012;Young et al, 2015;Charles and Nixon, 2019). Among the last are measures such as electroencephalography (EEG), electromyography (EMG), electrocardiography (ECG), galvanic skin response (GSR), inertial measurements, and speech (Chen et al, 2012).…”

“…The advantage of physiological measurements lies in their objectivity and, in recent years, in the low-cost and ease of deployment of body-worn sensors. Many studies have measured task loading using a limited number of physiological signals such as eye movement, or EEG, or GSR in an attempt to measure task loading along one or few dimensions (Smallwood and Schooler, 2006;Feng et al, 2013;Lean and Shan, 2012;Charles and Nixon, 2019). However, few studies have combined modalities so that estimation error can be reduced while classification accuracy can be increased (Chen et al, 2012).…”