Thermal Imaging as a Way to Classify Cognitive Workload

John Stemberger; Robert S Allison; Thomas Schnell

doi:10.1109/CRV.2010.37

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Conference proceeding

Thermal Imaging as a Way to Classify Cognitive Workload

John Stemberger, Robert S Allison and Thomas Schnell

2010 Canadian Conference on Computer and Robot Vision, pp.231-238

05/2010

DOI: 10.1109/CRV.2010.37

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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.

Aerospace electronics

Artificial neural networks

Biometrics

Cognition

Head

Infrared imaging

Laboratories

Measurement standards

Performance Evaluation Techniques

Real-time sensing and control

Robustness

Skin

Temperature measurement

Thermal Imaging

Workload

Details

Title: Subtitle: Thermal Imaging as a Way to Classify Cognitive Workload
Creators: John Stemberger - York University
Robert S Allison - York University
Thomas Schnell - University of Iowa
Resource Type: Conference proceeding
Publication Details: 2010 Canadian Conference on Computer and Robot Vision, pp.231-238
DOI: 10.1109/CRV.2010.37
Publisher: IEEE
Language: English
Date published: 05/2010
Academic Unit: Neurology; Electrical and Computer Engineering; Occupational and Environmental Health; Industrial and Systems Engineering; Public Policy Center (Archive); Mechanical Engineering
Record Identifier: 9984186969302771

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