Xuanyou Liu (Zed)

Wrist electrical impedance tomography offers a camera-free way to sense hand activity on wearables, but fitting this sensing into everyday device form factors remains difficult. We investigate a smartwatch-oriented design that embeds planar electrodes in a conventional watch-back layout and evaluate its ability to support gesture recognition in user studies. The results suggest that integrating EIT into familiar wearable hardware is a practical direction for unobtrusive input.

Wrist electrical impedance tomography can enable privacy-preserving hand sensing without cameras, but everyday use depends on how stable that sensing remains as the arm moves. We examine this posture dependence with wearable data collected across different users, hand configurations, and arm positions. The findings show that arm posture can strongly influence sensing quality and that broader posture coverage during training improves generalization to unseen conditions. We also explore learning approaches aimed at improving robustness across users and postures.

Inertial odometry using only IMUs offers a lightweight approach to human motion tracking for augmented reality and wearables, but learning-based methods often drift because they lack explicit models of human motion dynamics. We propose MARIO, which grounds inertial odometry in human kinematics through a learned pose prior inferred from IMU data, promoting physically consistent motion during state propagation. We further develop a sensor-fusion framework that incorporates auxiliary signals from lightweight sensors already available on commercial AR glasses, including magnetometers, barometers, and secondary IMUs. Evaluated on a large-scale motion dataset, MARIO substantially reduces positional drift compared with strong IO baselines and improves robustness across diverse motion conditions, pointing toward lightweight, camera-free human tracking that unifies kinematic priors with multimodal sensing.

Sensory substitution devices enable users to perceive visual information through other modalities, such as touch. However, most existing devices place the camera at the user's eyes (head-mounted), which limits the ability to coordinate manual interactions. We propose "seeing and feeling" from the hand's perspective to enhance the flexibility and expressivity of sensory substitution. To this end, we engineered a back-of-the-hand electrotactile display that renders tactile images from a wrist-mounted camera, allowing users to feel objects while reaching and hovering. In a user study with sighted and blind or low-vision participants, we compared our hand-centered perspective against traditional head-mounted views in manipulation tasks (e.g., handling bottles, soldering). Results indicate that while both perspectives yield comparable performance, participants preferred the flexibility of the hand's perspective, which supported more ergonomic object manipulation strategies.

Electrotactile stimulation offers a promising path for high-resolution wearable haptics but has traditionally struggled with integration into soft, everyday textiles. We present TacTex, a textile interface that seamlessly integrates high-density haptic feedback and touch sensing. By employing a novel multi-layer woven structure that separates conductive electrodes with non-conductive yarns, TacTex achieves a sensing and actuation resolution of 512 × 512 with electrode spacing as small as 2mm. The system includes a custom driving board that enables precise spatial and temporal control of electrical stimuli while simultaneously monitoring voltage changes for touch tracking. Our technical evaluation and user studies demonstrate that TacTex can render a wide range of haptic effects, including complex static and dynamic patterns, effectively bringing high-fidelity haptic feedback to everyday clothing.