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Te-yen Wu

Publications and source records attributed to Te-yen Wu.

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KnitID: Machine-Knitted RFID Antennas for Battery-Free Authentication, Localization and Interaction

Battery-free RFID systems offer a scalable and maintenance-free approach to interaction. We present KnitID, a machine-knitted textile RFID antenna design that enables on-body authentication, localization, and interaction. Unlike prior antenna designs, KnitID achieves a compact antenna form factor (60mm by 8mm) by integrating magnet wire into the unique loop-over-loop structure of machine knitting. This structure reduces the size of conventional loop antennas by around 90\%, while also providing 30\% longer sensing ranges than standard dipole designs with similar size on the human body. The compact form factor creates new opportunities to embed multiple RFID tags across the human body, enriching backscatter signals and supporting a broader range of battery-free on-body interactions. To demonstrate this capability, we build an interactive sleeve to support wearer authentication, spatial localization, and interaction detection. Through technical evaluations, we show the feasibility of KnitID to provide diverse and battery-free interactions on knitted user interfaces.

cs.HC

BIT: Battery-free, IC-less and Wireless Smart Textile Interface and Sensing System

The development of smart textile interfaces is hindered by the inclusion of rigid hardware components and batteries within the fabric, which pose challenges in terms of manufacturability, usability, and environmental concerns related to electronic waste. To mitigate these issues, we propose a smart textile interface and its wireless sensing system to eliminate the need for ICs, batteries, and connectors embedded into textiles. Our technique is established on the integration of multi-resonant circuits in smart textile interfaces, and utilizing near-field electromagnetic coupling between two coils to facilitate wireless power transfer and data acquisition from smart textile interface. A key aspect of our system is the development of a mathematical model that accurately represents the equivalent circuit of the sensing system. Using this model, we developed a novel algorithm to accurately estimate sensor signals based on changes in system impedance. Through simulation-based experiments and a user study, we demonstrate that our technique effectively supports multiple textile sensors of various types.

cs.HC