Improving pilot mental workload evaluation with combined measures

Wanyan, Xiaoru; Zhuang, Damin; Zhang, Huan

doi:10.3233/bme-141041

Cited by 42 publications

(36 citation statements)

References 14 publications

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“…NASA-TLX (Hart and Staveland 1988) (Backs, 1994;Brookings et al, 1996;Durantin et al, 2014;Fairclough et al, 2005;Fallahi et al, 2016;Fournier et al, 1999;Gao et al, 2013;Hankins and Wilson, 1998;Hoepf et al, 2015;Hsu et al, 2015;Hwang et al, 2008;Lee and Liu, 2003;Lehrer et al, 2010;Luque-Casado et al, 2016;Matthews et al, 2015;Miyake, 2001;Miyake et al, 2009;Ryu and Myung, 2005;Sauer et al, 2013;Sirevaag et al, 1993;Svensson and Wilson, 2009;Wanyan et al, 2014) 22 RSME (Zijlstra 1993) (Hogervorst et al, 2014;Hsu et al, 2015;De Rivecourt et al, 2008;Veltman and Gaillard 1996;Veltman and Gaillard 1998;Veltman 2002) 6…”

Section: Inclusion Criteriaunclassified

“…Total: 38 (Backs, 1994;Bernardi et al, 2000;Boutcher and Boutcher, 2006;Causse et al, 2010;Delaney and Brodie, 2000;Finsen et al, 2001;Hogervorst et al, 2014;Holland and Tarlow, 1972;Luque-Casado et al, 2016;Miyake et al, 2009;Nickel and Nachreiner, 2003;Reiner and Gelfeld, 2014;Ryu and Myung, 2005;Schellekens et al, 2000;Tripathi et al, 2003;Zhang et al, 2010) 16 Simulated fixed wing operations (Braby et al, 1993;Dussault et al, 2005;Fairclough et al, 2005;Fairclough and Venables 2006;Fournier et al, 1999;Hsu et al, 2015;Lahtinen et al, 2007;Lee and Liu 2003;Lehrer et al, 2010;Mansikka et al, 2016a;Mansikka et al, 2016b;Miyake 2001;Prinzel et al, 2000;De Rivecourt et al, 2008;Svensson and Wilson 2009;Tattersall and Hockey 1995;Veltman and Gaillard 1996;Veltman and Gaillard 1998;Wang et al, 2016;Wanyan et al, 2014) 19 Fixed-wing operations (Dussault et al, 2004;Hankins and W...…”

Section: Inclusion Criteriamentioning

confidence: 99%

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Measuring mental workload using physiological measures: A systematic review

2019

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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.

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Section: Inclusion Criteriaunclassified

Section: Inclusion Criteriamentioning

confidence: 99%

Measuring mental workload using physiological measures: A systematic review

2019

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“…Mean pupil diameter was sensitive to workload, and it increased gradually with the increase of workload. It indicated that a further resource was needed to deal with the increase of workload [ 28 ].…”

Section: Discussionmentioning

confidence: 99%

A comprehensive prediction and evaluation method of pilot workload

Feng

Wanyan

Yang

et al. 2018

THC

Self Cite

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BACKGROUND: The prediction and evaluation of pilot workload is a key problem in human factor airworthiness of cockpit.OBJECTIVE: A pilot traffic pattern task was designed in a flight simulation environment in order to carry out the pilot workload prediction and improve the evaluation method.METHODS: The prediction of typical flight subtasks and dynamic workloads (cruise, approach, and landing) were built up based on multiple resource theory, and a favorable validity was achieved by the correlation analysis verification between sensitive physiological data and the predicted value.RESULTS: Statistical analysis indicated that eye movement indices (fixation frequency, mean fixation time, saccade frequency, mean saccade time, and mean pupil diameter), Electrocardiogram indices (mean normal-to-normal interval and the ratio between low frequency and sum of low frequency and high frequency), and Electrodermal Activity indices (mean tonic and mean phasic) were all sensitive to typical workloads of subjects.CONCLUSION: A multinominal logistic regression model based on combination of physiological indices (fixation frequency, mean normal-to-normal interval, the ratio between low frequency and sum of low frequency and high frequency, and mean tonic) was constructed, and the discriminate accuracy was comparatively ideal with a rate of 84.85%.

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“…Some past studies have investigated the reliability of using a combined approach to assess mental workload for computerized tasks and simulated conditions in other domains (Cegarra & Chevalier, ; Luque‐Casado, Perales, Cárdenas, & Sanabria, ; Wanyan, Zhuang, & Zhang, ). Luque‐Casado et al () investigated HRV as a function of cognitive demands (based on the NASA‐TLX ratings) in several computerized tasks.…”

Section: Introductionmentioning

confidence: 99%

Assessment and monitoring of mental workload in subway train operations using physiological, subjective, and performance measures

Jafari

Zaeri

Jafari

et al. 2020

Hum Ftrs & Erg Mfg Svc

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Subway train operation is a complex, sociotechnical system that involves a variety of cognitively demanding tasks. The train operators are responsible for continuously monitoring the surrounding environment, maintaining awareness, processing information, and making decisions under risk. The resulting mental strain on operators can negatively affect their performance and the interaction of the human-machine system. The objective of this study was to evaluate if physiological, subjective, and performance measures could identify the level of mental workloads arising from routine and nonroutine operations in the subway system. A total of 11 subway train operators underwent different driving scenarios in a high-fidelity simulator. The simulated tasks were divided into two categories: routine operations (preparing to drive and driving between stations without interruptions or emergencies) and nonroutine operation (responding to a tunnel fire, dealing with a high density of passengers, encountering a passenger/technician on the track, and responding to train failure). The mental workload was monitored and evaluated in these tasks using an electrocardiogram, subjective self-rating scales, and driving performance. Both heart rate variability and performance measures (including reaction time and error rate) detected mental workload variations in the different operations. On the other hand, the subjective ratings (including NASA-TLX) assessed the overall mental workload associated with a task, without explaining the mental demand variations within the task over time. Subway train drivers experienced different levels of mental workload during routine and nonroutine driving conditions.

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Improving pilot mental workload evaluation with combined measures

Cited by 42 publications

References 14 publications

Measuring mental workload using physiological measures: A systematic review

Measuring mental workload using physiological measures: A systematic review

A comprehensive prediction and evaluation method of pilot workload

Assessment and monitoring of mental workload in subway train operations using physiological, subjective, and performance measures

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