Enabling Saccade Latency Measurements with Consumer-Grade Cameras

Abstract

Eye movements can be affected by a number of neurological, neuromuscular, and neurodegenerative disorders that are important to diagnose and track longitudinally. To enable unobtrusive tracking of disease progression, we tailored and evaluated a set of candidate eye-tracking algorithms to operate on video sequences obtained from an iPhone 6, for accurate and robust determination of the time between the presentation of a visual stimulus and the beginning of the eye movement toward the stimulus (saccade latency). Additionally, we proposed a model-based method to determine the onset of the eye movement and demonstrate that the associated residual normalized root-mean-squared error can be used to automatically flag saccade tracings that should not be included in further analysis. A variant of the iTracker algorithm performs most robustly and results in mean saccade latencies and associated standard deviations on iPhone recordings that are essentially the same as those obtained from simultaneous recordings using a high-end, high-speed camera. Our results suggest that accurate and robust saccade latency determination is feasible using consumer-grade cameras and therefore might enable unobtrusive tracking of neurodegenerative disease progression.