Abstract
An Intelligent Adaptive System (IAS) is a synergy between an intelligent interface and adaptive automation technologies capable of context sensitive interaction with operators. A well-designed IAS should enable flexible task allocation between the operator and the machine. Research suggests that the integration of real-time operator state assessment (e.g., performance, psychophysiology) can create a true ‘human-in-the-loop’ system, thereby minimizing deleterious performance effects such as overlooking automation failures and slowly reorienting to tasks. However, these research approaches apply a variety of methodologies to determine sensors, metrics, and overall system design when applied to real world tasks. This paper seeks to untangle these issues such that a more comprehensive framework for systematically evaluating the utility of cognitive state detection methods is attainable.
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Hou, M., Fidopiastis, C.M. (2014). Untangling Operator Monitoring Approaches When Designing Intelligent Adaptive Systems for Operational Environments. In: Schmorrow, D.D., Fidopiastis, C.M. (eds) Foundations of Augmented Cognition. Advancing Human Performance and Decision-Making through Adaptive Systems. AC 2014. Lecture Notes in Computer Science(), vol 8534. Springer, Cham. https://doi.org/10.1007/978-3-319-07527-3_3
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DOI: https://doi.org/10.1007/978-3-319-07527-3_3
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