Reconstructing realistic digital twins has become crucial as advances in mixed reality, metaverse, and robotics demand more accurate simulations for the physical world. Despite technical progress, building high-fidelity digital twins from a systematic and human-centered perspective remains underexplored. Drawing from the human processing model, we decompose human-centric reality into perception, motion, and cognition, and define a reality-preserving digital twin (RPDT) as a reconstruction integrating these dimensions. We present RealTwin, an attribute-graph-based representation and inference framework for RPDT. Leveraging the grounding capabilities of Multimodal Large Language Models (MLLMs), RealTwin chains AI tools to construct attribute graphs that faithfully encode real-world properties. We validate RealTwin through both technical evaluation, showing promising success in graph parsing and attribute inference, and a user study, assessing its applicability across diverse user groups. Enlightened by RealTwin, we discuss critical issues, including ecology, interaction space, and real-world adoption, for future end-to-end, fine-grained, and scalable digital twin reconstruction.

In Extended Reality (XR), complex acoustic environments often overwhelm users, compromising both scene awareness and social engagement due to entangled sound sources. We introduce MoXaRt, a real-time XR system that uses audio-visual cues to separate these sources and enable fine-grained sound interaction. MoXaRt’s core is a cascaded architecture that performs coarse, audio-only separation in parallel with visual detection of sources (e.g., faces, instruments). These visual anchors then guide refinement networks to isolate individual sources, separating complex mixes of up to 5 concurrent sources (e.g., 2 voices + 3 instruments) with ∼ 2 second processing latency. We validate MoXaRt through a technical evaluation on a new dataset of 30 one-minute recordings featuring concurrent speech and music, and a 22-participant user study. Empirical results indicate that our system significantly enhances speech intelligibility, yielding a 36.2% (p < 0.01) increase in listening comprehension within adversarial acoustic environments while substantially reducing cognitive load (p < 0.001), thereby paving the way for more perceptive and socially adept XR experiences.

Past research has widely explored the design and fabrication of resistive matrix-based tactile sensors for creating touch-sensitive devices. However, real-world deployment of resistive tactile sensing systems remains difficult for individuals with limited prior experience in embedded sensing due to challenges of portability, adaptivity, and efficiency. We introduce the WiReSens Toolkit, an accessible, open-source platform to bridge this gap. Central to our approach is adaptive hardware for interfacing with resistive sensors and a web-based GUI that streamlines access to advanced features for building scalable tactile sensing systems, including multi-device programming and wireless visualization across three communication protocols, autocalibration for adaptive sensitivity, and intermittent data transmission for low-power use. We validated the toolkit’s usability through a user study with 11 novice participants, who, on average, configured a tactile sensor with over 95% accuracy in under five minutes, calibrated sensors 10× faster than baseline methods, and showed improved sense-making of tactile data.

Real-time hand pose estimation has a wide range of applications spanning gaming, robotics, and human-computer interaction. In this paper, we introduce EITPose, a wrist-worn, continuous 3D hand pose estimation approach that uses eight electrodes positioned around the forearm to model its interior impedance distribution during pose articulation. Unlike wrist-worn systems relying on cameras, EITPose has a slim profile (12 mm thick sensing strap) and is power-efficient (consuming only 0.3 W of power), making it an excellent candidate for integration into consumer electronic devices. In a user study involving 22 participants, EITPose achieves with a within-session mean per joint positional error of 11.06 mm. Its camera-free design prioritizes user privacy, yet it maintains cross-session and cross-user accuracy levels comparable to camera-based wrist-worn systems, thus making EITPose a promising technology for practical hand pose estimation.

Despite recent exponential advancements in computer vision and reinforcement learning, it remains challenging for robots to interact with liquids. These challenges are particularly pronounced due to the limitations imposed by opaque containers, transparent liquids, fine-grained splashes, and visual obstructions arising from the robot’s own manipulation activities. Yet, there exists a substantial opportunity for robotics to excel in liquid identification and manipulation, given its potential role in chemical handling in laboratories and various manufacturing sectors such as pharmaceuticals or beverages. In this work, we present a novel approach for liquid class identification and state estimation leveraging electrical impedance sensing. We design and mount a digitally embroidered electrode array to a commercial robot gripper. Coupled with a customized impedance sensing board, we collect data on liquid manipulation with a swept frequency sensing mode and a frequency-specific impedance measuring mode. Our developed learning-based model achieves an accuracy of 93.33% in classifying 9 different types of liquids (8 liquids + air), and 97.65% in estimating the liquid state. We investigate the effectiveness of our system with a series of ablation studies. These findings highlight our work as a promising solution for enhancing robotic manipulation in liquid-related tasks.

In this paper, we present PortaChrome, a portable light source that can be attached to everyday objects to reprogram the color and texture of surfaces that come in contact with them. When PortaChrome makes contact with objects previously coated with photochromic dye, the UV and RGB LEDs inside PortaChrome create multi-color textures on the objects. In contrast to prior work, which used projectors for the color-change, PortaChrome has a thin and flexible form factor, which allows the color-change process to be integrated into everyday user interaction. Because of the close distance between the light source and the photochromic object, PortaChrome creates color textures in less than 4 minutes on average, which is 8 times faster than prior work. We demonstrate PortaChrome with four application examples, including data visualizations on textiles and dynamic designs on wearables.

We introduce MechSense, 3D-printed rotary encoders that can be fabricated in one pass alongside rotational mechanisms, and report on their angular position, direction of rotation, and speed. MechSense encoders utilize capacitive sensing by integrating a floating capacitor into the rotating element and three capacitive sensor patches in the stationary part of the mechanism. Unlike existing rotary encoders, MechSense does not require manual assembly but can be seamlessly integrated during design and fabrication. Our MechSense editor allows users to integrate the encoder with a rotating mechanism and exports files for 3D-printing. We contribute a sensor topology and a computational model that can compensate for print deviations. Our technical evaluation shows that MechSense can detect the angular position (mean error: 1.4°) across multiple prints and rotations, different spacing between sensor patches, and different sizes of sensors. We demonstrate MechSense through three application examples on 3D-printed tools, tangible UIs, and gearboxes.

We present FlexBoard, an interaction prototyping platform that enables rapid prototyping with interactive components such as sensors, actuators and displays on curved and deformable objects. FlexBoard offers the rapid prototyping capabilities of traditional breadboards but is also flexible to conform to different shapes and materials. FlexBoard’s bendability is enabled by replacing the rigid body of a breadboard with a flexible living hinge that holds the metal strips from a traditional breadboard while maintaining the standard pin spacing. In addition, FlexBoards are also shape-customizable as they can be cut to a specific length and joined together to form larger prototyping areas. We discuss FlexBoard’s mechanical design and present a technical evaluation of its bendability, adhesion to curved and deformable surfaces, and holding force of electronic components. Finally, we show the usefulness of FlexBoard through 3 application scenarios with interactive textiles, curved tangible user interfaces, and VR.

This paper aims to break the boundary between VR and IoT devices by creating interactions between 2D screens and the 3D virtual environment. Since headsets are expensive and not accessible to everyone, we aim to create a lower boundary for people to take part in the VR world using their smartphones. Existing research mostly focuses on creating new haptic devices for VR, we instead leverage existing IoT devices that people already own, such as smartphones, to make VR technologies more accessible to multiple IoT users. There are two parts in our project: azimuth detection and communication between the VR environment and IoT devices.

In this paper, we introduce MagKnitic, a novel approach to integrate passive force feedback and binary sensing into fabrics via digital machine knitting. Our approach utilizes digital fabrication technology to enable haptic interfaces that are soft, flexible, lightweight, and conform to the user’s body shape. Despite these characteristics, our interfaces provide diverse, interactive, and responsive force feedback, expanding the design space for haptic experiences.MagKnitic provides scalable and customizable passive haptic sensations by utilizing the attractive force between ferromagnetic yarns and permanent magnets, both of which are seamlessly integrated into knitted fabrics. Moreover, we present a binary sensing capability based on the resistance drop resulting from the activated electrical path between the integrated magnets and ferromagnetic yarn upon direct contact. We offer parametric design templates for users to customize MagKnitic layouts and patterns. With various design layouts and combinations, MagKnitic supports passive haptics interactions of linear, polar, angular, planar, radial, and user-defined motions. We perform a technical evaluation of the passive force feedback and the binary sensing capabilities with different machine knitting layouts and patterns, embedded magnet sizes, and interaction distances. In addition, we conduct two user studies to validate the effectiveness of MagKnitic. Finally, we demonstrate various application scenarios, including wearable input interfaces, game controllers, passive VR/AR wearables, and interactive furniture coverings.

In this paper, we propose SensorViz, a visualization tool that supports novice makers during different stages of prototyping with sensors. SensorViz provides three modes of visualization: (1) visualizing datasheet specifications before buying sensors, (2) visualizing sensor interaction with the environment via AR before building the physical prototype, and (3) visualizing live/recorded sensor data to test the assembled prototype. SensorViz includes a library of visualization primitives for different types of sensor data and a sensor database builder, which once a new sensor is added automatically creates a matching visualization by composing visualization primitives. Our user study with 12 makers shows that users are more effective in selecting sensors and configuring sensor layouts using SensorViz compared to traditional prototyping utilizing datasheets and manual testing on the prototype. Our post hoc interviews indicate that SensorViz reduces trial and error by allowing makers to explore sensor positions on the prototype early in the design process.

Unsupervised physical rehabilitation traditionally has used motion tracking to determine correct exercise execution. However, motion tracking is not representative of the assessment of physical therapists, which focus on muscle engagement. In this paper, we investigate if monitoring and visualizing muscle engagement during unsupervised physical rehabilitation improves the execution accuracy of therapeutic exercises by showing users whether they target the right muscle groups. To accomplish this, we use wearable electrical impedance tomography (EIT) to monitor muscle engagement and visualize the current state on a virtual muscle-skeleton avatar. We use additional optical motion tracking to also monitor the user’s movement. We conducted a user study with 10 participants that compares exercise execution while seeing muscle + motion data vs. motion data only, and also presented the recorded data to a group of physical therapists for post-rehabilitation analysis. The results indicate that monitoring and visualizing muscle engagement can improve both the therapeutic exercise accuracy during rehabilitation, and post-rehabilitation evaluation for physical therapists.

In this paper, we propose EIT-kit, an electrical impedance tomography toolkit for designing and fabricating health and motion sensing devices. EIT-kit contains (1) an extension to a 3D editor for personalizing the form factor of electrode arrays and electrode distribution, (2) a customized EIT sensing motherboard for performing the measurements, (3) a microcontroller library that automates signal calibration and facilitates data collection, and (4) an image reconstruction library for mobile devices for interpolating and visualizing the measured data. Together, these EIT-kit components allow for applications that require 2- or 4-terminal setups, up to 64 electrodes, and single or multiple (up to four) electrode arrays simultaneously.

CurveBoards are breadboards integrated into physical objects. In contrast to traditional breadboards, CurveBoards better preserve the object's look and feel while maintaining high circuit fluidity, which enables designers to exchange and reposition components during design iteration. Since CurveBoards are fully functional, i.e., the screens are displaying content and the buttons take user input, designers can test interactive scenarios and log interaction data on the physical prototype while still being able to make changes to the component layout and circuit design as needed. We present an interactive editor that enables users to convert 3D models into CurveBoards and discuss our fabrication technique for making CurveBoard prototypes. We also provide a technical evaluation of CurveBoard's conductivity and durability and summarize informal user feedback.

MorphSensor is a 3D electronic design tool that enables designers to morph existing sensor modules of pre-defined two-dimensional shape into free-form electronic component arrangements that better integrate with the three-dimensional shape of a physical prototype. MorphSensor builds onto existing sensor module schematics that already define the electronic components and the wiring required to build the sensor. Since MorphSensor maintains the wire connections throughout the editing process, the sensor remains fully functional even when designers change the electronic component layout on the prototype geometry. We detail the MorphSensor editor that supports designers in re-arranging the electronic components, and discuss a fabrication pipeline based on customized PCB footprints for making the resulting freeform sensor. We then demonstrate the capabilities of our system by morphing a range of sensor modules of different complexity and provide a technical evaluation of the quality of the resulting free-form sensors.

In this paper, we propose a different perspective on the use of support material: rather than printing support structures for overhangs, our idea is to make use of its transient nature, i.e. the fact that it can be dissolved when placed in a solvent, such as water. This enables a range of new use cases, such as quickly dissolving and replacing parts of a prototype during design iteration, printing temporary assembly labels directly on the object that leave no marks when dissolved, and creating time-dependent mechanisms, such as fading in parts of an image in a shadow art piece or releasing relaxing scents from a 3D printed structure sequentially overnight. Since we use regular support material (PVA), our approach works on consumer 3D printers without any modifications. To facilitate the design of objects that leverage dissolvable support, we built a custom 3D editor plugin that includes a simulation showing how support material dissolves over time. In our evaluation, our simulation predicted geometries that are statistically similar to the example shapes within 10% error across all samples.

Although cost-effective at-home blood pressure monitors are available, a complementary mobile solution can ease the burden of measuring BP at critical points throughout the day. In this work, we developed and evaluated a smartphone-based BP monitoring application called textitSeismo. The technique relies on measuring the time between the opening of the aortic valve and the pulse later reaching a periphery arterial site. It uses the smartphone's accelerometer to measure the vibration caused by the heart valve movements and the smartphone's camera to measure the pulse at the fingertip. The system was evaluated in a nine participant longitudinal BP perturbation study. Each participant participated in four sessions that involved stationary biking at multiple intensities. The Pearson correlation coefficient of the blood pressure estimation across participants is 0.20-0.77 ($mu$=0.55, $sigma$=0.19), with an RMSE of 3.3-9.2 mmHg ($mu$=5.2, $sigma$=2.0).