Tian MIN | 閔 天

Virtual Inertial Measurement Units (IMUs) offer a promising approach to generating synthetic motion data for training and evaluating human activity recognition (HAR) systems. However, existing virtual IMU workflows remain fragmented and technically demanding, requiring users to switch between 3D editors, scripting tools, and offline signal processing pipelines. These limitations hinder usability and iterative refinement for researchers and developers who design HAR systems. We present Vsens, an XR-based system that unifies virtual IMU configuration, visualization, and data synthesis within an immersive workspace. Vsens allows developers to directly manipulate sensor placements on digital avatars and observe synthesized IMU signals in real time. To understand how developers with different expertise levels benefit from such an environment, we conducted a user study with 20 HAR developers covering a broad range of experience levels. Results show that Vsens improves configuration efficiency and fosters deeper spatial understanding of sensor behavior across the participants. Beyond usability, our findings reveal how embodied interaction and real-time feedback inform the design of practical virtual data collection systems that foster more effective and human-centered workflows.

Recent advances in large language models (LLMs) have enabled the emergence of intelligent agents capable of performing complex multi-step tasks across various domains. In parallel, the growth of Web3 has introduced a decentralized web infrastructure, yet remains largely inaccessible to non-technical users due to operational complexity, fragmented information, and security risks. In this paper, we present Web3Agent, an AI agent system that integrates LLM-based interaction with blockchain environments to enable language-driven on-chain operations. Web3Agent automatically decomposes user instructions into structured workflows, dynamically queries blockchain data and APIs, and performs multi-step operations such as asset transfers, token swaps, and smart contract execution. Web3Agent incorporates real-time inspection, error handling, and interaction transparency across its operation log, and flow visualization components. We evaluate the system and perform ablation study with customized dataset in a simulated environment, demonstrating its feasibility in orchestrating complex Web3 tasks and highlighting implications for agent-based abstraction in decentralized systems.

Soil-based Microbial Fuel Cells (SMFCs) offer a sustainable method for powering low-energy computing devices by harnessing electricity from microbial activity in soil. In this paper, we introduce SoilSense, a novel approach that repurposes SMFCs as tangible interfaces, transforming soil into an interactive, computationally responsive medium, instead of energy sources. We explore the voltage variations that occur when pressure is applied to the cathode and systematically characterize this mechanism across different electrode configurations and soil moisture levels. To demonstrate the feasibility of SMFC-based interfaces, we present a series of modular and proof-of-concept prototypes that support diverse interaction modalities. We further illustrate how SoilSense enables interactions through example applications and provide implications and envision for future studies to employ soil as an ecologically compatible material in interactive system design.

We present SoilTile, a novel ground sensing approach that explores soil as an interactive medium. Built upon the principles of Soil-based Microbial Fuel Cells (SMFCs), SoilTile draws on the natural electrochemical interactions between soil and embedded electrodes—not as a source of energy, but as a way to sense physical contact through electrical response. When pressure applied to the cathode, SMFC produce measurable variations in output voltage. Building on this phenomenon, we construct modular ground sensing units that detect localized pressure through a matrix of electrodes, structured to respond under human activities or object placement. With its minimalist structure, DIY-friendly fabrication, and potential for seamless integration into natural environments, SoilTile offers a simple, material-driven platform for designing interactive spaces.

The emergence of virtual sensors in recent years has opened up new possibilities for the development of human activity recognition (HAR) systems. For instance, we can synthesized virtual sensor data for those scarce datasets, such as accelerometer data, from the widely available multimedia resources online through cross-modal approaches. However, existing solutions on virtual sensors primarily focus on batch pipelines, relying heavily on lengthy processing workflows and sophisticated computer vision techniques, which often lack interactivity and flexibility for the usage in customized and small-scale scenarios. In this work, we present the Vsens Toolkit, an AR-based open-ended system for virtual sensors, which serves as an preliminary exploration of the user interface for virtual sensors. It integrates functionalities such as scene construction, data collection, data augmentation, and visualization. In this interactivity demonstration, we showcase exemplar scenarios including wearable accelerometers, capacitive sensing, wrist-worn sensor tracking, and sandbox for free exploration (Figure 1).

Guiding users with limb exercise can assist in muscle training or physical recovery. However, traditional vision-based methods often require multiple camera angles to help users understand the motions and require them to be within the range of the screen. Therefore, we propose a non-visual system that can guide users with multiple-directional limb motions utilizing spatial audio, AudioMove, with commercial-off-the-shelf (COTS) devices (i.e., smartphones and earphones). The proposed system addresses the challenge of conveying directional information encompassing multiple planes in real-time. We conduct a mixed-method user study to evaluate the effectiveness of the system with three methods combining motion data with spatial audio perception. Additionally, a user interface is built to collect users’ comments. The results conclude that spatial audio guidance could create a natural, pervasive, and non-visual exercise training solution in daily life.

The rise of the Internet of Things (IoT) has given birth to transformative and massively deployed computing applications that raise the significant issue of energy sources. It is impractical and irresponsible to rely on wires and batteries to power trillion-level devices. One promising prediction is that energy harvesting technologies will serve as alternative power sources for IoT devices. However, we might be losing this prophecy for lack of understanding of how novice developers comprehend energy in developing IoT. In response, we conducted a mentored physical prototyping study with a two-day workshop involving eight novice developers. The study consisted of qualitative and quantitative analyses, the artifacts, interviews with both novice developers and an expert, and implications of designs for future tools. The findings reveal informational gaps that demand educational efforts and assistive features to facilitate novice developers. We present major findings from the study and implications for the design of future tools.

Human activities can introduce variations in various environmental cues, such as light and sound, which can serve as inputs for interfaces. However, one often overlooked aspect is the airflow variation caused by these activities, which presents challenges in detection and utilization due to its intangible nature. In this paper, we have unveiled an approach using mist to capture invisible airflow variations, rendering them detectable by Time-of-Flight (ToF) sensors. We investigate the capability of this sensing technique under different types of mist or smoke, as well as the impact of airflow speed. To illustrate the feasibility of this concept, we created a prototype using a humidifier and demonstrated its capability to recognize motions. On this basis, we introduce potential applications, discuss inherent limitations, and provide design lessons grounded in mist-based airflow sensing.

Regardless of which community, incentivizing users is a necessity for well-sustainable operations. In the blockchain-backed Web3 communities, known for their transparency and security, airdrop serves as a widespread incentive mechanism for allocating capital and power. However, it remains a controversy on how to justify airdrop to incentive and empower the decentralized governance. In this paper, we use ParaSwap as an example to propose a role taxonomy methodology through a data-driven study to understand the characteristic of community members and the effectiveness of airdrop. We find that users receive more rewards tend to take positive actions towards the community. We summarize several arbitrage patterns and confirm the current detection is not sufficient in screening out airdrop hunters. In conjunction with the results, we discuss from the aspects of interaction, financialization, and system design to conclude the challenges and possible research directions for decentralized communities.