Current mobile hand tracking systems primarily rely on high-framerate (HFR) optical sensors to capture hand positions, resulting in high computational cost and limiting the applicability in end devices. We propose 3DRing, a 3D hand position tracking method that requires only low-framerate (LFR, <10 FPS) optical data and a single IMU ring. It consists of two stages: (1) a Deep Extended Kalman Filter module that predicts high-framerate hand positions from LFR optical measurements and a single IMU; (2) a Reinforcement Learning module that adaptively selects minimal keyframes for calibration, further reducing the average optical framerate. Using only 6.61 FPS optical data, 3DRing achieves an average real-time tracking error of 1.75 cm and an interaction efficiency of 86.0% in a 3D target selection task, compared to the 67 FPS hand tracking system of Meta Quest Pro, demonstrating a strong potential to reduce the reliance on optical data in mobile hand tracking tasks.

LLMs are now embedded in a wide range of everyday scenarios. However, their inherent hallucinations risk hiding misinformation in fluent responses, raising concerns about overreliance on AI. Detecting overreliance is challenging, as it often arises in complex, dynamic contexts and cannot be easily captured by post-hoc task outcomes. In this work, we aim to investigate how users’ behavioral patterns correlate with overreliance. We collected interaction logs from 77 participants working with an LLM injected plausible misinformation across three real-world tasks and we assessed overreliance by whether participants detected and corrected these errors. By semantically encoding and clustering segments of user interactions, we identified five behavioral patterns linked to overreliance: users with low overreliance show careful task comprehension and fine-grained navigation; users with high overreliance show frequent copy-paste, skipping initial comprehension, repeated LLM references, coarse locating, and accepting misinformation despite hesitation. We discuss design implications for mitigation.

Long-range Human-Robot Interaction (HRI) remains underexplored. Within it, Command Source Identification (CSI) — determining who issued a command — is especially challenging due to multi-user and distance-induced sensor ambiguity. We introduce HiSync, an optical-inertial fusion framework that treats hand motion as binding cues by aligning robot-mounted camera optical flow with hand-worn IMU signals. We first elicit a user-defined (N=12) gesture set and collect a multimodal command gesture dataset (N=38) in long-range multi-user HRI scenarios. Next, HiSync extracts frequency-domain hand motion features from both camera and IMU data, and a learned CSINet denoises IMU readings, temporally aligns modalities, and performs distance-aware multi-window fusion to compute cross-modal similarity of subtle, natural gestures, enabling robust CSI. In three-person scenes up to 34 m, HiSync achieves 92.32% CSI accuracy, outperforming the prior SOTA by 48.44%. HiSync is also validated on real-robot deployment. By making CSI reliable and natural, HiSync provides a practical primitive and design guidance for public-space HRI.

We introduce Thermotion, a novel method using thermofluidic composites to design and display thermochromic animation effects on object surfaces. With fluidic channels embedded under the object surfaces, the composites utilize thermofluidic flows to dynamically control the surface temperature as an actuator for thermochromic paints, which enables researchers and designers for the first time to create animations not only on two and three-dimensional surfaces but also on the surface made of a few flexible everyday materials. We report the design space with six animation primitives and two modification effects, and we demonstrate the design and fabrication workflow with a customized software platform for design and simulation. A range of applications is shown leveraging the objects’ dynamic displays both visually and thermally, including dynamic artifacts, teaching aids, and ambient displays. We envision an opportunity to extend thermofluidic composites to other heat-related practices for further dynamic and programmable interactions with temperature.