Students often take digital notes during live lectures, but current methods can be slow when capturing information from lecture slides or the instructor's speech, and require them to focus on their devices, leading to distractions and missing important details. This paper explores supporting live lecture note-taking with mixed reality (MR) to quickly capture lecture information and take notes while staying engaged with the lecture. A survey and interviews with university students revealed common note-taking behaviors and challenges to inform the design. We present MaRginalia to provide digital note-taking with a stylus tablet and MR headset. Students can take notes with an MR representation of the tablet, lecture slides, and audio transcript without looking down at their device. When preferred, students can also perform detailed interactions by looking at the physical tablet. We demonstrate the feasibility and usefulness of MaRginalia and MR-based note-taking in a user study with 12 students.

Novel technologies such as augmented reality and computer perception lay the foundation for smart assistants that can guide us through real-world tasks, such as cooking or home repair. However, the nature of real-world interaction requires assistants that adapt to users’ mistakes, environments, and communication preferences. We propose Adaptive Multimodal Assistants (AMMA), a software architecture for task guidance with generated adaptive interfaces from step-by-step instructions. This is achieved through 1) an automatically generated user action state tracker and 2) a guidance planner that leverages a continuously trained user model. The assistant also adjusts its guidance and communication delivery methods based on observed user performance as well as implicit and explicit user feedback. We demonstrated the viability of AMMA by building an adaptive cooking assistant running in a high-fidelity virtual reality-based simulator. A user study of the cooking assistant showed that AMMA can reduce the task completion time and the number of manual communication methods changes.

Gaze-based target suffers from low input precision and target occlusion. In this paper, we explored to leverage the continuous eyelid movement to support high-efficient and occlusion-robust dwell-based gaze pointing in virtual reality. We first conducted two user studies to examine the users’ eyelid movement pattern both in unintentional and intentional conditions. The results proved the feasibility of leveraging intentional eyelid movement that was distinguishable with natural movements for input. We also tested the participants’ dwelling pattern for targets with different sizes and locations. Based on these results, we propose DEEP, a novel technique that enables the users to see through occlusions by controlling the aperture angle of their eyelids and dwell to select the targets with the help of a probabilistic input prediction model. Evaluation results showed that DEEP with dynamic depth and location selection incorporation significantly outperformed its static variants, as well as a naive dwelling baseline technique. Even for 100% occluded targets, it could achieve an average selection speed of 2.5s with an error rate of 2.3%.