CAPturAR: An Augmented Reality Tool for Authoring Human-Involved Context-Aware Applications (UIST2020)
Tianyi Wang*, Xun Qian*, Fengming He, Xiyun Hu, Ke Huo, Yuanzhi Cao, and Karthik Ramani. "CAPturAR: An Augmented Reality Tool for Authoring Human-Involved Context-Aware Applications." In Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology (UIST2020) DOI: https://doi.org/10.1145/3379337.3415815
Abstract: Recognition of human behavior plays an important role in context-aware applications. However, it is still a challenge for end-users to build personalized applications that accurately recognize their own activities. Therefore, we present CAPturAR, an in-situ programming tool that supports users to rapidly author context-aware applications by referring to their previous activities. We customize an AR head-mounted device with multiple camera systems that allow for non-intrusive capturing of user’s daily activities. During authoring, we reconstruct the captured data in AR with an animated avatar and use virtual icons to represent the surrounding environment. With our visual programming interface, users create human-centered rules for the applications and experience them instantly in AR. We further demonstrate four use cases enabled by CAPturAR. Also, we verify the effectiveness of the AR-HMD and the authoring workflow with a system evaluation using our prototype. Moreover, we conduct a remote user study in an AR simulator to evaluate the usability.