A systematic approach to developing near real-time performance predictions based on physiological measures

Kraft, Amanda; Russo, Jon C.; Krein, Michael; Russell, Bartlett; Casebeer, William D.; Ziegler, Matthias

doi:10.1109/cogsima.2017.7929601

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…However, Kraft et al (2017) recently achieved similar accuracy scores in using deep artificial neural networks and radial kernel-based support vector machines that computed up to 26 inputs from EEG, ECG, and EOG sensors. However, our method used only pupil diameter.…”

Section: Discussionmentioning

confidence: 99%

Real-time Prediction of User Performance based on Pupillary Assessment via Eye-Tracking

Buettner¹,

Sauer²,

Maier³

et al. 2018

THCI

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Abstract:We propose a method to predict user performance based on eye-tracking. The method uses eye-tracking-based pupillometry to capture pupil diameter data and calculates-based on a Random Forest algorithm-user performance expectations. We conducted a large-scale experimental evaluation (125 participants aged from 21 to 61 years) and found promising results that pave the way for a dynamic real-time adaption of IT to a user's mental effort and expected user performance. We have already achieved a good classification accuracy of user performance after only 40 seconds (5% of the mean total trial time that our participants took to complete our experiment). The non-invasive contact-free method can be applied cost-efficiently both in research and practical environments.

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Section: Discussionmentioning

confidence: 99%

Real-time Prediction of User Performance based on Pupillary Assessment via Eye-Tracking

Buettner¹,

Sauer²,

Maier³

et al. 2018

THCI

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“…Measuring and combining many different psychophysiological measures also presents a set of challenges that researchers have grappled with [39]. Cognitive workload presents differently through the systems being measured (e.g., heart-rate, speech or the brain's electrical activity) so making more than one of these data sources available for the assessment should make the monitoring more robust and accurate.…”

Section: Challenges In Assessing Cognitive Workloadmentioning

confidence: 99%

Assessing Cognitive Workload Using Cardiovascular Measures and Voice

Magnúsdóttir

Jóhannsdóttir

Majumdar

et al. 2022

Sensors

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Monitoring cognitive workload has the potential to improve both the performance and fidelity of human decision making. However, previous efforts towards discriminating further than binary levels (e.g., low/high or neutral/high) in cognitive workload classification have not been successful. This lack of sensitivity in cognitive workload measurements might be due to individual differences as well as inadequate methodology used to analyse the measured signal. In this paper, a method that combines the speech signal with cardiovascular measurements for screen and heartbeat classification is introduced. For validation, speech and cardiovascular signals from 97 university participants and 20 airline pilot participants were collected while cognitive stimuli of varying difficulty level were induced with the Stroop colour/word test. For the trinary classification scheme (low, medium, high cognitive workload) the prominent result using classifiers trained on each participant achieved 15.17 ± 0.79% and 17.38 ± 1.85% average misclassification rates indicating good discrimination at three levels of cognitive workload. Combining cardiovascular and speech measures synchronized to each heartbeat and consolidated with short-term dynamic measures might therefore provide enhanced sensitivity in cognitive workload monitoring. The results show that the influence of individual differences is a limiting factor for a generic classification and highlights the need for research to focus on methods that incorporate individual differences to achieve even better results. This method can potentially be used to measure and monitor workload in real time in operational environments.

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“…A second potential application of this method is real-time operator state assessment and adaptive automation such as described in Scerbo et al (2001) and Kraft et al (2017). Modelbased workload assessment is beyond the scope of this thesis.…”

Section: Applicationsmentioning

confidence: 99%

A comparison of linear and nonlinear ECG-based methods to assess pilot workload in a live-flight tactical setting

Reichlen¹

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Daniel McGehee for their mentorship through this process. In addition, I would like to thank all members of the Industrial Engineering faculty for their contributions to my educational development over the last two years. I would like to thank USAF and AFIT for affording me to opportunity to pursue a graduate degree at the University of Iowa. I would also like to thank the 711 HPW and AFRL for funding and allowing me to contribute to the spatial disorientation research which motivated this thesis. Lastly, I would like to thank my wife Cat for her love, support, and most of all, patience throughout the course of my graduate studies.

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A systematic approach to developing near real-time performance predictions based on physiological measures

Cited by 6 publications

References 13 publications

Real-time Prediction of User Performance based on Pupillary Assessment via Eye-Tracking

Real-time Prediction of User Performance based on Pupillary Assessment via Eye-Tracking

Assessing Cognitive Workload Using Cardiovascular Measures and Voice

A comparison of linear and nonlinear ECG-based methods to assess pilot workload in a live-flight tactical setting

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