Where to Draw the Line: Physical Space Partitioning and View Privacy in AR-based Co-located Collaboration for Immersive Analytics
Inoussa Ouedraogo, Huyen Nguyen, Patrick Bourdot · 2024
This paper investigates two main aspects of co-located collaboration using Augmented Reality (AR) for Immersive Analytics (IA): physical space partitioning and view privacy. AR-based collaborative work in IA can greatly benefit from direct conversational awareness cues and enhanced mutual understanding between users. However, some challenges still exist, particularly in enabling efficient interaction for IA tasks such as analysis and decision-making on complex data within limited physical space. Moreover, collaborative IA often involves both cooperative and individual tasks with experts of diverse backgrounds, necessitating effective workspace management. To address spatial proximity issues in limited space such as offices or meeting rooms, we explored a workspace partitioning approach that divided physical space with virtual boundaries on the floor. We conducted a user study to examine workspace management approaches (partitioning and non-partitioning) in conjunction with view privacy policies (public and private view). Findings suggest that under private view conditions, individual tasks were completed more quickly, and non-partitioning facilitated faster placement of shared objects. Additionally, public view improved object arrangement time in partitioned space.