Analysis of the sensitivity of heart rate variability and subjective workload measures in a driving simulator: The case of highway work zones

Shakouri, Mahmoud; Ikuma, Laura H.; Aghazadeh, Fereydoun; Nahmens, Isabelina

doi:10.1016/j.ergon.2018.02.015

Cited by 68 publications

(31 citation statements)

References 37 publications

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“…Some inaccuracies can be expected from such results because the subjective measurement is sensitive to pilots' interpretation, judgment and psycho-social state. In particular, pilots may tend to have a high self-estimation and underestimate difficulties, which may jeopardize the reliability of the data [39,40].…”

Section: Subjective Measurementsmentioning

confidence: 99%

Aircraft Pilots Workload Analysis: Heart Rate Variability Objective Measures and NASA-Task Load Index Subjective Evaluation

et al. 2020

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Workload and fatigue of aircraft pilots represent an argument of great interest in the framework of human factors and a pivotal point to be considered in aviation safety. 75% of aircraft accidents are related to human errors that, in most cases, are due to high level of mental workload and fatigue. There exist several subjective or objective metrics to quantify the pilots’ workload level, with both linear and nonlinear relationships reported in the literature. The main research objective of the present work is to analyze the relationships between objective and subjective workload measurements by looking for a correlation between metrics belonging to the subjective and biometric rating methods. More particularly, the Heart Rate Variability (HRV) is used for the objective analysis, whereas the NASA-TLX questionnaire is the tool chosen for the subjective evaluation of the workload. Two different flight scenarios were considered for the studies: the take-off phase with the initial climb and the final approach phase with the landing. A Maneuver Error Index (MEI) is also introduced to evaluate the pilot flight performance according to mission requirements. Both qualitative and quantitative correlation analyses were performed among the MEI, subjective and objective measurements. Monotonic relationships were found within the HRV indexes, and a nonlinear relationship is proposed among NASA-TLX and HRV indexes. These findings suggest that the relationship between workload, biometric data, and performance indexes are characterized by intricate patterns of nonlinear relationships.

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Section: Subjective Measurementsmentioning

confidence: 99%

Aircraft Pilots Workload Analysis: Heart Rate Variability Objective Measures and NASA-Task Load Index Subjective Evaluation

et al. 2020

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“…In (near) real-time, electroencephalography (EEG), event-related potentials (ERP) derived from EEG and functional Near Infrared Spectroscopy (fNIRS), for instance, can provide estimations of the electrical activity or cerebral blood flow to derive MWL levels from (Antonenko et al 2010;Ayaz et al 2012;Mehta and Parasuraman 2013). Other measures such as electro-dermal activity (also coined 'galvanic skin response'; Boucsein 2012), heart rate variability (Aasman et al 1987;Shakouri et al 2018) or pupillometry (Backs et al 2003), try to capture reactions of the autonomous nervous system to approximate MWL.…”

Section: Empirical Referentsmentioning

confidence: 99%

Understanding mental workload: from a clarifying concept analysis toward an implementable framework

et al. 2018

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The growing need for mental workload (MWL) optimization on the shop floor yields an impressive increase in theoretical and applied references to the concept of mental workload (Young et al. 2014). However, do we really understand and agree upon what mental workload exactly is? Does it include emotional load? Can we rely upon an explanatory framework? The present account first runs a critical concept analysis on mental workload, based on the Walker and Avant (2011) method. Results show that existing definitions and theoretical accounts arbitrarily include and exclude defining variables and describe these variables on various levels of abstraction, misuse pivotal terms such as mediation and moderation, and do not theoretically explicitate the role of yet repeatedly operationalized emotional load variables such as frustration. We therefore clarify the concept by disentangling MWL into its antecedents, defining attributes and consequences. Next, we derive a clearcut conceptual definition and present a generic explanatory frameworkthe latter extended with insights from Cognitive Load Theory (Sweller 1988; 1994). We conclude with a set of suggestions for future research and practice. Next to contributing to the theoretical clarification of this hallmark concept, the concept analysis and derived explanatory framework, as proposed, can foster solid research practices and support practitioners in contextualizing MWL-assessment and in effectively optimizing MWL.

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“…Typically, direct objective measurements of performance are utilized, including task accuracy, timing, and failure rates [9][10][11]. Neurophysiological data, such as pupillometry [12,13] and heart rate data [14], can be used as an indirect measure of human performance by serving as a measure of cognitive workload. Cognitive workload is a multidimensional construct used to represent the effort required by human working memory [15].…”

Section: Traditional Approaches To Quantify Human Performancementioning

confidence: 99%

Virtual Modeling of User Populations and Formative Design Parameters

Knisely

Vaughn-Cooke

2020

Systems

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Human variability related to physical, cognitive, socio-demographic, and other factors can contribute to large differences in human performance. Quantifying population heterogeneity can be useful for designers wishing to evaluate design parameters such that a system design is robust to this variability. Comprehensively integrating human variability in the design process poses many challenges, such as limited access to a statistically representative population and limited data collection resources. This paper discusses two virtual population modeling approaches intended to be performed prior to in-person design validation studies to minimize these challenges by: (1) targeting recruitment of representative population strata and (2) reducing the candidate design parameters being validated in the target population. The first approach suggests the use of digital human models, virtual representations of humans that can simulate system interaction to eliminate candidate design parameters. The second approach suggests the use of existing human databases to identify relevant human characteristics for representative recruitment strata in subsequent studies. Two case studies are presented to demonstrate each approach, and the benefits and limitations of each are discussed. This paper demonstrates the benefit of modeling prior to conducting in-person human performance studies to minimize resource burden, which has significant implications on early design stages.

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Analysis of the sensitivity of heart rate variability and subjective workload measures in a driving simulator: The case of highway work zones

Cited by 68 publications

References 37 publications

Aircraft Pilots Workload Analysis: Heart Rate Variability Objective Measures and NASA-Task Load Index Subjective Evaluation

Aircraft Pilots Workload Analysis: Heart Rate Variability Objective Measures and NASA-Task Load Index Subjective Evaluation

Understanding mental workload: from a clarifying concept analysis toward an implementable framework

Virtual Modeling of User Populations and Formative Design Parameters

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