Xuanyou Liu | Closed-Loop HCI Researcher

About

I am an HCI researcher with a background across wearable sensing, haptic interfaces, and human-AI decision-making.

I am a graduate student in Computer Science at Northwestern University (MU Collective), advised by Prof. Jessica Hullman. I have built wearable sensing that lets machines read the body, and electrotactile interfaces that communicate information to the skin; I now study how people and AI leverage this information to collaboratively reason and decide under uncertainty.

Before Northwestern, I was a research assistant with Prof. Pedro Lopes at the University of Chicago (HC-Integration Lab), with Prof. Karan Ahuja at Northwestern (SPICE Lab), and with Prof. Teng Han at ISCAS. I received my M.S.E. in Robotics from the University of Pennsylvania (GRASP Lab), advised by Prof. Michelle Johnson, and a B.E. in Industrial Design from Xi'an Jiaotong University.

News

Mar 2026 Our paper "MARIO" was accepted to Findings of CVPR 2026!
Sep 2025 Started my graduate studies at Northwestern University!
May 2025 Graduated from UPenn with M.S.E. in Robotics.
Jan 2025 Our paper "Seeing with the Hands" was accepted to CHI 2025, and we presented an interactive demo at the conference!

Research

My work asks three connected questions: how can wearable sensing read subtle body activity in everyday form factors, how can electrotactile haptics communicate rich information through the skin, and how can people and AI reason together when decisions are uncertain?

Sensing · Machines sense people

EIT for Hand Estimation

Exploring Electrical Impedance Tomography (EIT) as a novel sensing modality for continuous hand pose estimation, with a focus on integrating and miniaturizing sensing hardware for low-power, unobtrusive wearable input.

Northwestern EIT Machine Learning

Intelligence · Humans and AI decide

Human-AI Interaction

Investigating how people reason and make decisions alongside AI systems, with a focus on uncertainty communication, AI-advised decision-making, and designing interfaces that align with human cognitive processes.

Northwestern Human-AI Interaction Decision-Making

Stimulation · People feel machines

Wearable Haptics

Developing compact, high-resolution electrotactile interfaces that deliver rich tactile feedback through the skin, enabling new forms of sensory augmentation and communication for wearable devices.

UChicago UPenn Haptics Wearables

Publications

* denotes equal contribution

Smartwatch-oriented wrist EIT gesture sensing prototype

UIST 2026 To Appear

Integrating Wrist EIT Gesture Sensing into Smartwatch Hardware

Xuanyou Liu, Novel Alam, Karan Ahuja

ACM Symposium on User Interface Software and Technology

Video

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.

MARIO - Multi-sensor inertial odometry with human pose prior for AR tracking

CVPR 2026

MARIO: Motion-Augmented Real-Time Multi-Sensor Inertial Odometry

Yiquan Li*, Taeyoung Yeon*, Chenfeng Gao, Vasco Xu, Xuanyou Liu, Karan Ahuja

Findings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

PDF DOI

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.

Seeing with the Hands - Hand-mounted electrotactile display for sensory substitution

CHI 2025

Seeing with the Hands: A Sensory Substitution That Supports Manual Interactions

Also presented as an interactive demo at CHI 2025

Shan-Yuan Teng*, Gene Kim*, Xuanyou Liu*, Pedro Lopes

ACM Conference on Human Factors in Computing Systems

PDF Video DOI

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.

TacTex - High-resolution electrotactile textile interface with woven electrode structure

CHI 2024

TacTex: A Textile Interface with Seamlessly-Integrated Electrodes for High-Resolution Electrotactile Stimulation

Hongnan Lin, Xuanyou Liu, Shengsheng Jiang, Qi Wang, Ye Tao, Guanyun Wang, Wei Sun, Teng Han, Feng Tian

ACM Conference on Human Factors in Computing Systems

PDF Video DOI

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.

Teaching & Service

Reviewer

Serving as a reviewer for IMWUT 2026 (Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies).

IMWUT 2026

Teaching Assistant: ESE5190 Smart Devices

Graduate course at University of Pennsylvania covering embedded systems, IoT, and smart device development.

University of Pennsylvania

Code Instructor

Teaching Python & Arduino programming fundamentals to middle school students at Fife-Penn CS Academy.

Fife-Penn CS Academy

Student Representative

Represented students at the Global Youth Forum 2021, organized by the International Labour Organization.

ILO Global Youth Forum 2021

Contact

[email protected]

Room 3538, Mudd Building
Department of Computer Science
Northwestern University
Evanston, IL

Let's Connect

Feel free to reach out if you're interested in human-AI interaction, novel sensing & haptic technologies, or wearable computing. I'm happy to discuss research collaborations, exchange ideas, or just chat about the field!