Name
Title | ||
|---|---|---|
Untangling Operator Monitoring Approaches When Designing Intelligent Adaptive Systems for Operational Environments. |
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-07527-3_3 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Intelligent tutoring systems,adaptive automation,augmented cognition,psychophysiological measures,cognitive state | Psychophysiology,Communication,Intelligent interface,Computer science,Adaptive system,Augmented cognition,Systems design,Automation,Human–computer interaction,Operator (computer programming),Cognition | Conference |
Volume | ISSN | Citations |
8534 | 0302-9743 | 1 |
PageRank | References | Authors |
0.37 | 9 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming Hou | 1 | 65 | 9.09 |
| Cali M. Fidopiastis | 2 | 22 | 6.23 |