Assessment of saccadic eye movements in healthy subjects using consumer-grade mobile devices

Assessing eye movement features may provide insight into neurological health, inform diagnoses, and guide clinical intervention. The potential to utilize saccadic eye movement latency is especially promising as a clinical biomarker in identifying and treating neurodegenerative disease. Artificial intelligence and deep learning technology have improved the feasibility of eye-tracking methodology and scalability in research studies. Tablet and smartphone-based tracking equipment have been shown to provide quantitative data of comparable accuracy to more costly, special-built equipment while reducing cost and complexity in experimental procedures. Establishing an efficient and accurate measurement tool to aid the detection and tracking of diseases may benefit the development of comprehensive treatment and monitoring strategies. This study, therefore, seeks to examine oculomotor function through saccade latency and error rate in healthy adults with respect to age, demonstrating a mobile device’s efficacy in assessing subtle eye movements and establishing a dataset upon which to guide further investigation