Real-time gaze transition entropy

Abstract

In this video, we introduce a real-time algorithm that computes gaze transition entropy. This approach can be employed in detecting higher level cognitive states such as situation awareness. We first compute fixations using our real-time version of a well established velocity threshold based algorithm. We then compute the gaze transition entropy for a content independent grid of areas of interest in real-time using an update processing window approach. We test for Markov property after each update to test whether Markov assumption holds. Higher entropy corresponds to increased eye movement and more frequent monitoring of the visual field. In contrast, lower entropy corresponds to fewer eye movements and less frequent monitoring. Based on entropy levels, the system could then alert the user accordingly and plausibly offer an intervention. We developed an example application to demonstrate the use of the online calculation of gaze transition entropy in a practical scenario.