Thermal imaging as a way to classify cognitive workload. Stemberger, J., Allison, R., & Schnell, T. In Seventh Canadian Conference on Computer and Robot Vision (CRV2010), Ottawa, Canada, May 31st- June 2nd, 2010, 2010. -1 -2 doi abstract bibtex As epitomized in DARPA's 'Augmented Cognition' program, next generation avionics suites are envisioned as sensing, inferring, responding to and ultimately enhancing the cognitive state and capabilities of the pilot. Inferring such complex behavioural states from imagery of the face is a challenging task and multimodal approaches have been favoured for robustness. We have developed and evaluated the feasibility of a system for estimation of cognitive workload levels based on analysis of facial skin temperature. The system is based on thermal infrared imaging of the face, head pose estimation, measurement of the temperature variation across regions of the face and an artificial neural network classifier. The technique was evaluated in a controlled laboratory experiment using subjective measures of workload across tasks as a standard. The system was capable of accurately classifying mental workload into high, medium and low workload levels 81% of the time. The suitability of facial thermography for integration into a multimodal augmented cognition sensor suite is discussed.
@inproceedings{Stemberger:2010qm,
abstract = {As epitomized in DARPA's 'Augmented Cognition' program, next generation avionics suites are envisioned as sensing, inferring, responding to and ultimately enhancing the cognitive state and capabilities of the pilot. Inferring such complex behavioural states from imagery of the face is a challenging task and multimodal approaches have been favoured for robustness. We have developed and evaluated the feasibility of a system for estimation of cognitive
workload levels based on analysis of facial skin temperature. The system is based on thermal infrared imaging of the face, head pose estimation, measurement of the temperature variation across regions of the face and an artificial neural network classifier. The technique was evaluated in a controlled laboratory experiment using subjective measures of workload across tasks as a standard. The system was capable of accurately classifying mental workload into high, medium and low workload levels 81\% of the time. The suitability of facial thermography for integration into a multimodal augmented cognition sensor suite is discussed.},
address = {Ottawa, Canada},
author = {Stemberger, J. and Allison, R.S. and Schnell, T.},
booktitle = {Seventh Canadian Conference on Computer and Robot Vision (CRV2010)},
date-added = {2011-05-06 12:12:04 -0400},
date-modified = {2011-05-18 16:22:26 -0400},
doi = {10.1109/CRV.2010.37},
keywords = {Neural Avionics},
month = {May 31st- June 2nd, 2010},
title = {Thermal imaging as a way to classify cognitive workload},
url-1 = {http://dx.doi.org/10.1109/CRV.2010.37},
url-2 = {http://dx.doi.org/10.1109/CRV.2010.37},
year = {2010},
url-1 = {https://doi.org/10.1109/CRV.2010.37}}
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