Knitting RFID yarns towards smart fabrics for motion sensing and identification

Abstract Smart textiles integrate fabric comfort and flexibility with electronic functionality, offering transformative potential in healthcare, sports monitoring, and human-machine interfaces. However, most existing wearable systems rely on external circuit boards that compromise comfort, washability, and mechanical robustness. Here, we present a scalable textile-electronics integration strategy based on custom-engineered ID-Yarns ( ~ 500 μm in diameter) that embed semiconductor Radio-Frequency Identification (RFID) chips and antennas and can be seamlessly incorporated into fabrics using standard knitting processes. Using parameterized knitting geometries, we demonstrate two functional fabrics: a stretchable, interference-resistant Sensing-Fabric for precise motion detection, and a stretchable, deformation-insensitive Identification-Fabric (ID-Fabric) for stable passive wireless communication. Both fabrics exhibit strong mechanical endurance, maintaining functionality after more than 10,000 stretching and twisting cycles and over 20 machine washes while preserving consistent wireless performance. This work establishes a passive, yarn-level fiber-chip integration framework and provides a scalable solution toward soft, durable, and high-performance e-textiles through the synergistic design of yarn architecture and knitting-based antenna engineering.

Authors

Publication Details

Journal
npj Flexible Electronics
Published
2026-07-22
DOI
https://doi.org/10.1038/s41528-026-00619-4
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Knitting RFID yarns towards smart fabrics for motion sensing and identification

Weiwei Li, Lulu Xu, R Yang, William Whittow et al.
npj Flexible Electronics
Advanced Sensor and Energy Harvesting Materials
article

Knitting RFID yarns towards smart fabrics for motion sensing and identification

Weiwei Li, Lulu Xu, R Yang, William Whittow, Simon Hodder, Fangli Chen, Terry Tao Ye, Zhengzheng Li
article en

Abstract

Abstract Smart textiles integrate fabric comfort and flexibility with electronic functionality, offering transformative potential in healthcare, sports monitoring, and human-machine interfaces. However, most existing wearable systems rely on external circuit boards that compromise comfort, washability, and mechanical robustness. Here, we present a scalable textile-electronics integration strategy based on custom-engineered ID-Yarns ( ~ 500 μm in diameter) that embed semiconductor Radio-Frequency Identification (RFID) chips and antennas and can be seamlessly incorporated into fabrics using standard knitting processes. Using parameterized knitting geometries, we demonstrate two functional fabrics: a stretchable, interference-resistant Sensing-Fabric for precise motion detection, and a stretchable, deformation-insensitive Identification-Fabric (ID-Fabric) for stable passive wireless communication. Both fabrics exhibit strong mechanical endurance, maintaining functionality after more than 10,000 stretching and twisting cycles and over 20 machine washes while preserving consistent wireless performance. This work establishes a passive, yarn-level fiber-chip integration framework and provides a scalable solution toward soft, durable, and high-performance e-textiles through the synergistic design of yarn architecture and knitting-based antenna engineering.

npj Flexible Electronics
Openalex Percentile: Top 15%
Advanced Sensor and Energy Harvesting Materials
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.