Latest revision as of 13:07, 16 February 2021

Abstract

Future air traffic is expected to grow increasingly, opening up a gap for task dependent automation and adaptive interfaces, helping the Air Traffic Controller to cope with fluctuating workloads. One of the challenging factors in the application of such intelligent systems concerns the question what the operator is doing in order to optimize support and minimize automation surprises. This study questions whether eye metrics are able to determine what task the operator is engages in. We therefore examined A) if the eye-path would differ for three different ATC tasks and B) whether this effect can be quantified with six eye-metrics. In an experiment, the six eye-metrics were calculated and used as a dependent variable. The results show that some tasks can be inferred by eye movement metrics and other metrics infer workload, although none inferred by both task and workload. Copyright 2014 ACM.

Original document

The different versions of the original document can be found in:

• http://dx.doi.org/10.1145/2637248.2637274

• http://resolver.tudelft.nl/uuid:4eda5fcc-2ea6-46a4-9fac-da852bb9abe2

• https://research.vu.nl/en/publications/b8276063-0123-4331-8d71-b00d0dfdbd40

• https://core.ac.uk/display/90474991,

https://www.narcis.nl/publication/RecordID/oai%3Atudelft.nl%3Auuid%3A4eda5fcc-2ea6-46a4-9fac-da852bb9abe2,

https://repository.tudelft.nl/view/tno/uuid:4eda5fcc-2ea6-46a4-9fac-da852bb9abe2,

https://research.vu.nl/en/publications/eye-metrics-for-task-dependent-automation,

https://dblp.uni-trier.de/db/conf/ecce/ecce2014.html#ImantsG14,

https://dl.acm.org/citation.cfm?id=2637274,

https://academic.microsoft.com/#/detail/1969445452

• http://dl.acm.org/ft_gateway.cfm?id=2637274&ftid=1495385&dwn=1,

http://dx.doi.org/10.1145/2637248.2637274