June 2017
Volume 17, Issue 7
Open Access
OSA Fall Vision Meeting Abstract  |   June 2017
Identification of fixations in noisy eye movement data via recursive subdivision
Journal of Vision June 2017, Vol.17, 43. doi:10.1167/17.7.43
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      Jeffrey B. Mulligan, Donald J. Kalar; Identification of fixations in noisy eye movement data via recursive subdivision. Journal of Vision 2017;17(7):43. doi: 10.1167/17.7.43.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Human eye movements typically consist of a series of fixations (during which the eye is relatively still), linked by saccades, which rapidly reorient the direction of gaze to a new location. The locations fixated usually indicate the allocation of attention, and are useful when making inferences concerning state awareness in complex information environments, such as an aircraft cockpit. Identification of fixation events is straightforward when measurement noise is low (on the order of the physiological noise, typically a few arc minutes), but becomes increasingly challenging as noise increases to the levels encountered in current video-based remote tracking systems, which are suitable for installation in flight simulators. Here we present a novel method for identification of fixations in noisy eye position records. The method attempts to fit the signal with a piece-wise constant function, using an iterative method which recursively splits the data into two sub-intervals to produce the least RMS error in the fit. Proposed splits are accepted or rejected on the basis of a statistical t-test, with the level of significance providing a single parameter controlling the sensitivity. We compare the method to other position-based techniques, such as the classic “dispersion” method (which grows fixations rather than splitting as in our method), and a novel “breakout” method developed for the analysis of server log statistics.

Meeting abstract presented at the 2016 OSA Fall Vision Meeting

Footnotes
 Supported by the Technologies for Airplane State Awareness (TASA) project of NASA's Airspace Operations and Safety Program (AOSP).
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