The coordination of eye and head in visual scanning is a fundamental behavior of humans in everyday sensorimotor activities such as walking and navigation. Deep understanding of the spatiotemporal dynamics of this coordination behavior undoubtedly plays an important role for many fields. However, relatively little is known about the computational and high-order interactions between eye and head in visual scanning during sensorimotor tasks. In this paper, based on the utilization of a recent tool from information theory, Partial Information Decomposition (PID), we quantify high-order components, namely uniqueness, redundancy, and synergy, in spatiotemporal interactions between eye movement and head motion time-series data during augmented reality-based navigation experience. To our knowledge, this is the first data-driven approach that leverages an information-theoretic tool to characterize high-order interactions involved in eye-head coordination during sensorimotor activities.

AR head-mounted displays (HMDs) facilitate pedestrian navigation by integrating AR guidance into users' field of view (FOV). Displaying AR guidance using a body-fixed coordinate system has the potential to further leverage this integration by enabling users to control when the guidance appears in their FOV. However, it remains unclear how to effectively position AR guidance within this coordinate system during pedestrian navigation. Therefore, we explored the effects of three AR guidance positions (top, middle, and bottom) within a body-fixed coordinate system on pedestrian navigation in a virtual environment. Our results showed that AR guidance position significantly influenced eye movements, walking behaviors, and subjective evaluations. The top position resulted in the shortest duration of fixations on the guidance compared to the middle and bottom positions, and lower mental demand than the bottom position. The middle position had the smallest rate of vertical eye movement during gaze shifts between the guidance and the environment, and the smallest relative difference in walking speed between fixations on the guidance and the environment compared to the top and bottom positions. The bottom position led to the shortest duration and smallest amplitude of gaze shifts between the guidance and the environment compared to the top and middle positions, and lower frustration than the top position. Based on these findings, we offer design implications for AR guidance positioning within a body-fixed coordinate system during pedestrian navigation.

Assessing the drowsy state of driver is important for driving safety. In this paper, three kinds of information-theoretic tools are utilized to objectively and quantitatively measure the driver's detailed degrees of drowsiness. That is, fixation cross entropy (FCE) defined as the cross entropy between fixation count and duration, stationary gaze entropies based on fixation count (SGEc) and duration (SGEd) respectively, gaze transition entropy (GTE) based on fixation count are exploited to successfully obtain the drowsiness measurements. These measurements are based on eye-tracking data focusing on a novel division approach to area of interest (AOI), particularly emphasizing the significance of gaze deviation from the center point of screen. Psychophysical results showed that all the proposed measures are strongly correlated with Karolinska Sleepiness Scale (KSS), a widely used subjective measure of drowsiness. Particularly, compared with classical indicators for measuring drowsiness, FCE shows a stronger robustness under different drowsy conditions.