Oculomotor Plant Mathematical Model in Kalman Filter Form With Peak Velocity-Based Neural Pulse for Continuous Gaze Prediction

Abstract

An oculomotor plant mathematical model (OPMM) employs physical and neurological characteristics of human visual system to define its dynamics. One of its most prominent applications in modern eye-tracking pipelines was hypothesized to be latency reduction via the means of eye movement prediction. However, this use case was only explored with OPMMs originally designed for saccade simulation. Such models typically relied on the neural pulse control being estimated from intended saccade amplitude - a property that becomes fully observed only after a saccade already ended, which greatly limits the model’s prediction capabilities. We present the first OPMM designed with the prediction task in mind. We draw our inspiration from a “peak velocity - amplitude” main sequence relationship and propose to use saccade’s peak velocity for neural pulse estimation. We additionally extend the prior work by evaluating the proposed model on the largest to date pool of 322 subjects against the naive zero displacement baseline and a long short-term memory (LSTM) neural network.