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Multidimensional Real-Time Assessment of User State and Performance to Trigger Dynamic System Adaptation

  • Jessica SchwarzEmail author
  • Sven Fuchs
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10284)

Abstract

In adaptive human-machine interaction, technical systems adapt their behavior to the current state of the human operator to mitigate critical user states and performance decrements. While many researchers use measures of workload as triggers for adjusting levels of automation, we have proposed a more holistic approach to adaptive system design that includes a multidimensional assessment of user state. This paper outlines the design requirements, conceptual framework, and proof-of-concept implementation of a Real-time Assessment of Multidimensional User State (RASMUS). RASMUS diagnostics provide information on user performance, potentially critical user states, and their related impact factors on a second-by-second-basis in real-time. Based on these diagnoses adaptive systems are enabled to infer when the user needs support and to dynamically select and apply an appropriate adaptation strategy for a given situation. While the conceptual framework is generic, the implementation has been applied to an air surveillance task, providing real-time diagnoses for high workload, passive task-related fatigue and incorrect attentional focus.

Keywords

Multidimensional user state Physiological measures Workload Attention Fatigue Performance Real-time assessment Adaptation Augmented Cognition 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Fraunhofer Institute for Communication, Information Processing and Ergonomics, Department of Human-Systems EngineeringWachtbergGermany

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