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.

Tracking fine-grained finger movements with IMUs for continuous 2D-cursor control poses significant challenges due to limited sensing capabilities. Our findings suggest that finger-motion patterns and the inherent structure of joints provide beneficial physical knowledge, which lead us to enhance motion perception accuracy by integrating physical priors into ML models. We propose MouseRing, a novel ring-shaped IMU device that enables continuous finger-sliding on unmodified physical surfaces like a touchpad. A motion dataset was created using infrared cameras, touchpads, and IMUs. We then identified several useful physical constraints, such as joint co-planarity, rigid constraints, and velocity consistency. These principles help refine the finger-tracking predictions from an RNN model. By incorporating touch state detection as a cursor movement switch, we achieved precise cursor control. In a Fitts’ Law study, MouseRing demonstrated input efficiency comparable to touchpads. In real-world applications, MouseRing ensured robust, efficient input and good usability across various surfaces and body postures.

Traffic participant classification is crucial in autonomous driving perception. Millimeter wave radio detection and ranging radar is a cost-effective and powerful method to perform the task in adverse traffic scenarios, especially in bad inclement weather (e.g. fog, snow and rain) and poor lighting conditions. This paper presents an intelligent target classification algorithm for 77G radar based on correction data set. First, in order to handle the problem that the original data set may easily be interfered by obstacles, the angle information is filtered by analyzing the spatial information of radar signals, which means the interference clutter of obstacles can be effectively removed. Second, the primary data set is corrected using the significant difference between the micro-Doppler of the human body and car. Finally, the characteristic information of the radar signal is extracted, including distance, speed, orientation, micro-Doppler and reflection intensity, and the obtained data sets containing three types of targets (vehicles, human bodies and obstacles) are generated. The generated dynamic and static data sets are collected by sufficient experiments to construct deep learning classification models. The results show that the classification accuracy is improved by the measure of data set correction.