Video-Based Gaze Detection for Oculomotor Abnormality Measurements

Abstract

Measuring oculomotor abnormalities in human subjects is challenging due to the delicate spatio-temporal nature of the oculometric measures (OMs) used to assess eye movement abilities. Some OMs require a gaze estimation accuracy of less than 2 degrees and a sample rate that enables the detection of movements lasting less than 100 ms. While past studies and applications have used dedicated and limiting eye tracking devices to extract OMs, recent advances in imaging sensors and computer vision have enabled video-based gaze detection. Here, we present a self-calibrating neural network model for gaze detection that is suitable for oculomotor abnormality measurement applications. The model considers stimuli target locations while the examined subjects perform visual tasks and calibrate its gaze estimation output in real time. The model was validated in a clinical trial and achieved an axial accuracy of 0.93 degrees and 1.31 degrees for horizontal and vertical gaze estimation locations, respectively, as well as an absolute accuracy of 1.80 degrees. The performance of the proposed model enables the extraction of OMs using affordable and accessible setups—such as desktop computers and laptops—without the need to restrain the patient’s head or to use dedicated equipment. This newly introduced approach may significantly ease patient burden and improve clinical results in any medical field that requires eye movement measurements.