Exploring Trajectory Data in Augmented Reality : A Comparative Study of Interaction Modalities

Abstract

The visual exploration of trajectory data is crucial in domains such as animal behavior, molecular dynamics, and transportation. With the emergence of immersive technology, trajectory data, which is often inherently three-dimensional, can be analyzed in stereoscopic 3D, providing new opportunities for perception, engagement, and understanding. However, the interaction with the presented data remains a key challenge. While most applications depend on hand tracking, we see eye tracking as a promising yet under-explored interaction modality, while challenges such as imprecision or inadvertently triggered actions need to be addressed. In this work, we explore the potential of eye gaze interaction for the visual exploration of trajectory data within an AR environment. We integrate hand- and eye-based interaction techniques specifically designed for three common use cases and address known eye tracking challenges. We refine our techniques and setup based on a pilot user study (n=6) and find in a follow-up study (n=20) that gaze interaction can compete with hand-tracked interaction regarding effectiveness, efficiency, and task load for selection and cluster exploration tasks. However, time step analysis comes with higher answer times and task load. In general, we find the results and preferences to be user-dependent. Our work contributes to the field of immersive data exploration, underscoring the need for continued research on eye tracking interaction.