Objective-Analytical Measures of Workload – the Third Pillar of Workload Triangulation?

Rusnock, Christina F.; Borghetti, Brett J.; McQuaid, Ian

doi:10.1007/978-3-319-20816-9_13

Cited by 18 publications

(11 citation statements)

References 19 publications

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“…These demand values are then summed within and across resource channels for each point in time and, when graphed, form a workload profile. For more information on generating continuous workload profiles using IMPRINT see Rusnock et al (2015).…”

Section: Methods Neuroergonomic Model-development Process Overviewmentioning

confidence: 99%

Assessing Continuous Operator Workload With a Hybrid Scaffolded Neuroergonomic Modeling Approach

2017

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Objective: We aimed to predict operator workload from neurological data using statistical learning methods to fit neurological-to-state-assessment models.Background: Adaptive systems require real-time mental workload assessment to perform dynamic task allocations or operator augmentation as workload issues arise. Neuroergonomic measures have great potential for informing adaptive systems, and we combine these measures with models of task demand as well as information about critical events and performance to clarify the inherent ambiguity of interpretation.Method: We use machine learning algorithms on electroencephalogram (EEG) input to infer operator workload based upon Improved Performance Research Integration Tool workload model estimates.Results: Cross-participant models predict workload of other participants, statistically distinguishing between 62% of the workload changes. Machine learning models trained from Monte Carlo resampled workload profiles can be used in place of deterministic workload profiles for cross-participant modeling without incurring a significant decrease in machine learning model performance, suggesting that stochastic models can be used when limited training data are available.Conclusion: We employed a novel temporary scaffold of simulation-generated workload profile truth data during the model-fitting process. A continuous workload profile serves as the target to train our statistical machine learning models. Once trained, the workload profile scaffolding is removed and the trained model is used directly on neurophysiological data in future operator state assessments.Application: These modeling techniques demonstrate how to use neuroergonomic methods to develop operator state assessments, which can be employed in adaptive systems.

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Section: Methods Neuroergonomic Model-development Process Overviewmentioning

confidence: 99%

Assessing Continuous Operator Workload With a Hybrid Scaffolded Neuroergonomic Modeling Approach

2017

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“…Overall workload was assessed via a model tree classifier using electroencephalography, pupil dilation, blink rate, fixation duration, HR, HRV, and RR metrics (Rusnock, Borghetti, & McQuaid, 2015). An IMPRINT Pro overall workload model supervisory trained the classifier, which accurately assessed overall workload.…”

Section: Related Workmentioning

confidence: 99%

Multi-Dimensional Human Workload Assessment for Supervisory Human–Machine Teams

Heard

Adams

2019

Journal of Cognitive Engineering and Decision Making

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Humans commanding and monitoring robots’ actions are used in various high-stress environments, such as the Predator or MQ-9 Reaper remotely piloted unmanned aerial vehicles. The presence of stress and potential costly mistakes in these environments places considerable demands and workload on the human supervisors, which can reduce task performance. Performance may be augmented by implementing an adaptive workload human–machine teaming system that is capable of adjusting based on a human’s workload state. Such a teaming system requires a human workload assessment algorithm capable of estimating workload along multiple dimensions. A multi-dimensional algorithm that estimates workload in a supervisory environment is presented. The algorithm performs well in emulated real-world environments and generalizes across similar workload conditions and populations. This algorithm is a critical component for developing an adaptive human–robot teaming system that can adapt its interactions and intelligently (re-)allocate tasks in dynamic domains.

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“…The state-of-the-art workload assessment algorithms use the workload metrics as features to classify workload (e.g., References [3,27,45,53,56]). Rusnock et al [48] used a model tree classifier to estimate overall workload based on EEG, pupil dilation, blink rate, fixation duration, heart rate, heart-rate variability, and respiration rate. The classifier was trained on an IMPRINT Pro overall workload model and accurately assessed overall workload.…”

Section: Relevant Workmentioning

confidence: 99%

A Diagnostic Human Workload Assessment Algorithm for Collaborative and Supervisory Human--Robot Teams

Heard

Heald

Harriott

et al. 2019

J. Hum.-Robot Interact.

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High-stress environments, such as first-response or a NASA control room, require optimal task performance, as a single mistake may cause monetary loss or even the loss of human life. Robots can partner with humans in a collaborative or supervisory paradigm to augment the human's abilities and increase task performance. Such teaming paradigms require the robot to appropriately interact with the human without decreasing either's task performance. Workload is related to task performance; thus, a robot may use a human's workload state to modify its interactions with the human. Assessing the human's workload state may also allow for dynamic task (re-)allocation, as a robot can predict whether a task may overload the human and, if so, allocate it elsewhere. A diagnostic workload assessment algorithm that accurately estimates workload using results from two evaluations, one peer based and one supervisory based, is presented. The algorithm correctly classified workload at least 90% of the time when trained on data from the same human-robot teaming paradigm. This algorithm is an initial step toward robots that can adapt their interactions and intelligently (re-)allocate tasks. CCS Concepts: • Computing methodologies → Neural networks; • Computer systems organization → Robotic autonomy;

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Objective-Analytical Measures of Workload – the Third Pillar of Workload Triangulation?

Cited by 18 publications

References 19 publications

Assessing Continuous Operator Workload With a Hybrid Scaffolded Neuroergonomic Modeling Approach

Assessing Continuous Operator Workload With a Hybrid Scaffolded Neuroergonomic Modeling Approach

Multi-Dimensional Human Workload Assessment for Supervisory Human–Machine Teams

A Diagnostic Human Workload Assessment Algorithm for Collaborative and Supervisory Human--Robot Teams

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