FaceLight: Potentials and Drawbacks of Thermal Imaging to Infer Driver Stress

Andreas Riener, Bernhard Anzengruber

Research output: Chapter in Book/Report/Conference proceedingConference proceedings

Abstract

Driving a modern car is a complex, cognitive demanding task involving concentrated observation of the road, roadside, car and information/assistance system status, etc. Drivers are conscious about this, nevertheless, they are still operating tertiary controls, talking on the phone, smoking cigarettes, having lunch, reading maps or meeting agendas, or working on their computer. As a consequence - caused by cognitive overload and/or limited multitasking capabilities-- precarious driving situations are created. To explore the potential of thermal imaging in a vehicular setting, in particular to infer mental conditions of the driver in an unobtrusive manner, and to use this information to automatically react to a detected risky state, we have developed the prototypical interface "FaceLight" and performed a lab-based driving simulator study to evaluate the interface under conditions of varying workload. With "FaceLight" the driver can be interpreted as signal light, with a 'red face' (hot surface temperature) standing for high stress or cognitive overload while a 'green face' (cooler temperature) equals to a relaxed, stress-free mental state. Initial results revealed that this technology has potential to capture shifts in the mental state of an individual in a inattentive manner, but highlighted also that a lot of influencing factors still need to be incorporated to reliably recognize a specific state solely based on facial skin temperature (variation).
Original languageEnglish
Title of host publication4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI'12), October 17-19, Portsmouth, NH, USA
PublisherACM
Number of pages8
ISBN (Print)978-1-4503-1751-1
Publication statusPublished - Oct 2012

Fields of science

  • 102 Computer Sciences
  • 102009 Computer simulation
  • 102013 Human-computer interaction
  • 102019 Machine learning
  • 102020 Medical informatics
  • 102021 Pervasive computing
  • 102022 Software development
  • 102025 Distributed systems
  • 202017 Embedded systems
  • 211902 Assistive technologies
  • 211912 Product design

JKU Focus areas

  • Computation in Informatics and Mathematics
  • Engineering and Natural Sciences (in general)

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