Achieving touchpad-like pointing with a single IMU ring is highly desirable for portable and wearable interaction, yet challenging due to incomplete motion data and significant user variability. We present TraceRing, a finger-worn IMU system that enables precise two-dimensional cursor control. To address the limitations of generic end-to-end models, we propose a personalized training framework that learns user-specific representations through joint multi-task and contrastive learning, while dynamically selecting the most suitable expert model. This approach enables personalization without requiring per-user fine-tuning, and reduces velocity prediction error by 33.9% over state-of-the-art baselines. Furthermore, a real-time study shows it delivers speed and accuracy far exceeding those of AirMouse (2.26s v.s. 3.01s in average task completion time). These results demonstrate TraceRing as a portable and comfortable alternative for mobile computing and AR interaction applications.

Combining the use of an AR headset and a smartphone can provide wider display and precise touch input simultaneously; it can redefine the way we use applications today. Unfortunately, users are deprived of such benefit because of the independence of two devices. There lacks a kind of intuitive and direct interactions across them. In this paper, we conduct a formative study to understand the window management requirements and interaction preferences of using an AR headset and a smartphone simultaneously and report the insights we gained. Also, we introduce an example vocabulary of window management operations in AR headset + smartphone interface. It allows users to manipulate windows in virtual space and shift windows between devices efficiently and seamlessly.