Flight decks of the future are being enhanced through improved avionics that adapt to both aircraft and operator state. Eye tracking allows for non-invasive analysis of pilot eye movements, from which a set of metrics can be derived to effectively and reliably characterize workload, this research will generate quantitative algorithms to classify pilot state through eye tracking metrics. Through various metrics within the realm of eye tracking, flight deck operation research is used to determine metric correlations between a pilot's workload and eye tracking metric patterns. The basic metrics within eye tracking, such as saccadic movement, fixations and link analysis provide clear measurable elements that experimenters analyzed to create a quantitative algorithm that reliably classifies operator workload.
The study conducted at the University of Iowa's Operator Performance Lab 737-800 simulator was outfit with a Smarteye remote eye-tracking system that yielded gaze vector resolution down to 1 degree across the flight deck. Three levels of automation and 2 levels of outside visual conditions were changed on a KORD ILS approach between CAT II and CAT III visual conditions, and varying from full autopilot controlled by the pre-programmed flight management system, flight director guidance, and full manual approach with localizer and glide slope guidance. Initial subjective results indicated a successful variation in driving pilot workload across all 12 IFR pilots that were run through the 7 run testing sequence.
| Identifer | oai:union.ndltd.org:uiowa.edu/oai:ir.uiowa.edu:etd-1473 |
| Date | 01 July 2009 |
| Creators | Ellis, Kyle Kent Edward |
| Contributors | Schnell, Thomas |
| Publisher | University of Iowa |
| Source Sets | University of Iowa |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
| Source | Theses and Dissertations |
| Rights | Copyright 2009 Kyle Kent Edward Ellis |
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