Abstract
Optokinetic nystagmus (OKN) is an involuntary repeated “beating” of the eye, comprised of sequences of slow tracking (slow phase) and subsequent quick re-fixation events (quick phase) that occur in response to (typically horizontally) drifting stimuli. OKN has a characteristic saw-tooth pattern that we detect here using a state-machine approach applied to the eye-tracking signal. Our algorithm transitions through the slow/quick phases of nystagmus (and a final state) in order to register the start, peak and end points of individual sawtooth events. The method generates duration, amplitude, velocity estimates for candidate events, as well as repetition estimates from the signal.
We test the method on a small group of participants. The results suggest that false positive detections occur as single isolated events in feature space. As a result of this observation we apply a simple criteria based on the repetitious “beating” of the eye. The number of true positives is high (\(94\%\)) and false OKN detections are low (\(2\%\)). Future work will aim to optimise and rigorously validate the proof-of-concept framework we propose.
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Norouzifard, M., Black, J., Thompson, B., Klette, R., Turuwhenua, J. (2020). A Real-Time Eye Tracking Method for Detecting Optokinetic Nystagmus. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12047. Springer, Cham. https://doi.org/10.1007/978-3-030-41299-9_12
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