Determination of Saccade Latency Distributions using Video Recordings from Consumer-grade Devices

Quantitative and accurate tracking of neurocognitive decline remains an ongoing challenge. We seek to address this need by focusing on robust and unobtrusive measurement of saccade latency the time between the presentation of a visual stimulus and the initiation of an eye movement towards the stimulus which has been shown to he altered in patients with neurocognitive decline or neurodegenerative diseases. Here, we present a novel, deep convolutional-neural network-based method to measure saccade latency outside of the clinical environment using a smartphone camera without the need for supplemental or special-purpose illumination. We also describe a model-based approach to estimate saccade latency that is less sensitive to noise compared to conventional methods. With this flexible and robust system, we collected over 11,000 saccade-latency measurements from 21 healthy individuals and found distinctive saccade-latency distributions across subjects. When analyzing intra-subject variability across time, we observed noticeable variations in the mean saccade latency and associated standard deviation. We also observed a potential learning effect that should be further characterized and potentially accounted for when interpreting saccade latency measurements.