Comparison of multiple physiological sensors to classify operator state in adaptive automation systems

Taylor, Grant; Reinerman-Jones, Lauren; Cosenzo, Keryl; Nicholson, Denise

doi:10.1037/e578642012-002

Cited by 7 publications

(8 citation statements)

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“…Some of these measures include heart rate [13], blood pressure [14], chemical measures including sodium and potassium in saliva, cortisol in blood [14], heart rate variability [15], eye blink frequency [16], saccades [16], Electromyogram (EMG) [17] and EEG. Even though there seem not to be a best physiological indicator of workload, some studies showed EEG to be more promising compared to other indicators [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Mental Workload Estimation Using Wireless EEG Signals

Adewale

Panoutsos

2019

Preprint

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Previous studies have shown that electroencephalogram (EEG) can be used in estimating mental workload. However, developing fast and reliable models for cross-task, cross-subject and cross-session classifications of workload remains a challenge. In this study, a wireless Emotiv EPOC headset was used to evaluate workload in two different mental tasks: n-back task and mental arithmetic task. 0-back task and 2-back task were employed as low and high workload in the n-back task while 1-digit and 3-digit addition were used as the two different workload levels in the arithmetic task. Using power spectral density as features, a fast signal processing and feature extraction framework was developed to facilitate realtime estimation of workload. Within-session accuracies of 98.5% and 95.5% were achieved in the n-back and arithmetic tasks respectively. Adaptive subspace feature matching (ASFM) was applied for cross-session, cross-task and cross-subject classifications. The feature adaptation provided average crosssession accuracies of 80.5% and 74.4% in the n-back and the arithmetic tasks respectively. An average cross-task accuracy of 68.6% was achieved while cross-subject accuracies were 74.4% and 64.1% in the n-back and arithmetic tasks respectively. The framework generalised well across subjects and tasks, and it provided a promising approach towards developing subject and task-independent models. This study also shows that a consumerlevel wireless EEG headset can be applied in cognitive monitoring for real-time estimation of workload in practice.

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Section: Introductionmentioning

confidence: 99%

Mental Workload Estimation Using Wireless EEG Signals

Adewale

Panoutsos

2019

Preprint

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“…Heart Rate (HR) was computed using peak cardiac activity to measure the interval from each beat per second. The "So and Chan" QRS detection method was used to calculate Interbeat Interval (IBI) and Heart Rate Variability (HRV: Taylor, Reinerman-Jones, Cosenzo, & Nicholson, 2010). This approach maximizes the amplitude of the R-wave for more accurate determination of beat durations (Henelius, Hirvonen, Holm, Korpela, & Muller, 2009).…”

Section: Electrocardiogram (Ecg) the Advanced Brainmentioning

confidence: 99%

Multi-dimensional Workload in Two Types of Nuclear Power Plant Simulators

Reinerman-Jones

Lin

Barber

et al. 2019

Proceedings of the Human Factors and Ergonomics Society Annual

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Analog, full-scope, full-scale simulators with the fidelity to simulate all of the physical and underlying thermodynamics in the real system are representative of training simulators used by current operating nuclear power plants. However, digital simulators are becoming desirable to researchers and utility companies alike due to their increased accessibility and the capability of integrating new system upgrades. The present study compared operators’ workload response in a given operating procedure using an analog, full-scope/scale simulator and a digital, part-task simulator. Subjective measures (NASA-TLX, MRQ, ISA) and physiological measures (electrocardiography) were used to profile workload response. The results suggested the feasibility of using digital simulators for research purposes with potential future implications for training.

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“…Heart Rate (HR) was computed using peak cardiac activity to measure the interval from each beat per second. The "So and Chan" QRS detection method was used to calculate IBI and HRV (Taylor, Reinerman-Jones, Cosenzo, & Nicholson, 2010).…”

Section: Physiological Measuresmentioning

confidence: 99%

Workload Associated with Nuclear Power Plant Main Control Room Tasks

Reinerman-Jones

Mercado

D’Agostino³

et al. 2015

Proceedings of the Human Factors and Ergonomics Society Annual

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Nuclear Power Plant (NPP) operators complete a variety of tasks to ensure the NPP is running safely and efficiently. However, the levels and types of workload associated with the different task types are not yet fully understood. The present investigation examined workload levels and types for three common NPP Main Control Room (MCR) tasks in a controlled experimental environment using a variety of subjective, physiological, and performance measures of workload. The results suggest that the three task types differ in the levels and types of workload. These findings can be used to better understand the types of NPP tasks that induce workload and the type of workload they induce. The full results of these experiments will be captured in future articles and technical reports.

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Comparison of multiple physiological sensors to classify operator state in adaptive automation systems

Cited by 7 publications

References 0 publications

Mental Workload Estimation Using Wireless EEG Signals

Mental Workload Estimation Using Wireless EEG Signals

Multi-dimensional Workload in Two Types of Nuclear Power Plant Simulators

Workload Associated with Nuclear Power Plant Main Control Room Tasks

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