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Quantifying saccades while walking: validity of a novel velocity-based algorithm for mobile eye tracking.

Samuel Stuart, Brook Galna, Sue Lord

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 9, 2015
    PubMed
    Summary

    A new algorithm accurately detects saccades from mobile eye-tracking data during walking. This method reliably identifies eye movements in healthy adults and Parkinson's disease patients.

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    Area of Science:

    • Ophthalmology
    • Neurology
    • Biomedical Engineering

    Background:

    • Mobile eye-tracking offers insights into gaze behavior during natural activities like walking.
    • Accurate saccade detection is crucial for analyzing eye movement data in clinical populations.

    Purpose of the Study:

    • To validate a novel velocity threshold-based algorithm for detecting saccades from raw mobile infrared eye-tracking data.
    • To assess the algorithm's reliability in distinguishing saccades from artifacts like blinks and flickers.

    Main Methods:

    • Developed a velocity threshold algorithm to detect saccades from mobile eye-tracking data.
    • Collected data from healthy older adults and Parkinson's disease patients during walking.
    • Compared algorithm-detected saccades against a ground truth established by manual video inspection.

    Main Results:

    • The algorithm demonstrated high reliability compared to the ground truth (ICC(2,1) = 0.94).
    • Achieved an overall correct saccade detection rate of 85% across 100 trials from 10 subjects.
    • Effectively excluded artifacts such as blinks and flickers using specific criteria.

    Conclusions:

    • The validated algorithm provides a simple and robust method for analyzing mobile eye-tracking data.
    • This tool can facilitate research on gaze behavior in neurological conditions during real-world activities.