Workload Prediction, Diagnosis, and Continuing Challenges

Parks, Donald L.; BoucekJr., George P.

doi:10.1007/978-1-4757-9244-7_4

Cited by 29 publications

(13 citation statements)

References 3 publications

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“…One of the most important count variables, which can be employed in either single or multitask circumstances, is time: simple timeline analysis computes the ratio of time required to time available, or TR/TA (Parks and Boucek 1989). More specifically, timeline analysis will enable the system designer to profile the workload that operators encounter during a typical mission, such as landing an aircraft or starting up a power-generating plant (Kirwan and Ainsworth 1992).…”

Section: Defining Underload and Overload -The Elusive Redlinementioning

confidence: 99%

“…Thus, the workload of a mission would be computed by drawing lines representing different activities, of length proportional to their duration. The total length of the lines would be summed and then divided by the total time (Parks and Boucek 1989). In this way the workload encountered by or predicted for different members of a team (e.g.…”

Section: Defining Underload and Overload -The Elusive Redlinementioning

confidence: 99%

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State of science: mental workload in ergonomics

et al. 2014

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Mental workload (MWL) is one of the most widely used concepts in ergonomics and human factors and represents a topic of increasing importance. Since modern technology in many working environments imposes ever more cognitive demands upon operators while physical demands diminish, understanding how MWL impinges on performance is increasingly critical. Yet, MWL is also one of the most nebulous concepts, with numerous definitions and dimensions associated with it. Moreover, MWL research has had a tendency to focus on complex, often safety-critical systems (e.g. transport, process control). Here we provide a general overview of the current state of affairs regarding the understanding, measurement and application of MWL in the design of complex systems over the last three decades. We conclude by discussing contemporary challenges for applied research, such as the interaction between cognitive workload and physical workload, and the quantification of workload 'redlines' which specify when operators are approaching or exceeding their performance tolerances.

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Section: Defining Underload and Overload -The Elusive Redlinementioning

confidence: 99%

Section: Defining Underload and Overload -The Elusive Redlinementioning

confidence: 99%

State of science: mental workload in ergonomics

et al. 2014

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“…Although not applicable to the development of a measure of auditory workload, there was some question regarding the applicability of these models in developing a measure. However through our research (Sarno & Wickens, 1992;Aldrich, Szabo & Bierbaum, 1988;North & Riley, 1988;Parks & Boucek, 1988) it became clear that these models are more like calculated estimates of the eventual operator workload and do not actually measure it. Cohen, Wherry, and Glenn (1993) state that, "these estimates may not be valid indications of the real effort levels that will be required of operators when the actual system has been developed.…”

Section: Predictive Workload Modelsmentioning

confidence: 97%

Auditory Workload Assessment, Volume 1

Rench¹

2000

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“…These models are built from detailed task analyses and can incorporate individual behavior when the information is available. Examples of objective-analytical measures include time-line analysis and prediction (TLAP) (Parks and Boucek 1989), visual auditory cognitive psychomotor (Aldrich and Szabo 1986), and W/INDEX (North and Riley 1989). Advantages of objective-analytical measures include: (1) consistency in the workload values produced, (2) the ability to include workload values differentiated for resources or channels, and (3) the ability to calculate workload values on any time scale.…”

Section: Workload Measurement Taxonomymentioning

confidence: 99%

Foundations of Augmented Cognition

2015

Lecture Notes in Computer Science

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Abstract. Eye blinks and other ocular artifacts represent a dominant source of EEG signal interference, especially in frontal EEG electrodes. Even though there are several widely accepted methods for the removal eye-blinks, (e.g. linear filtering and ICA), it is still a difficult problem to address when the number of EEG electrodes are limited (as is the case for EEG systems designed for everyday application contexts), and dedicating a subset of these for monitoring eye activity is impractical. In this paper, we propose a novel and general method to eliminate the ocular artifacts based on a combination of filter banks and an eye tracker. This approach offers the promise of making non-intrusive, efficient, and robust ocular artifact detection and correction a tractable prospect.

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Workload Prediction, Diagnosis, and Continuing Challenges

Cited by 29 publications

References 3 publications

State of science: mental workload in ergonomics

State of science: mental workload in ergonomics

Auditory Workload Assessment, Volume 1

Foundations of Augmented Cognition

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