Flexible and Robust Metasurface‐Based Wearable Sensor for Intelligent Human Monitoring

L Long Chen (Department of Chemistry, Frontiers Science Center for New Organic Matter and State Key Laboratory of Advanced Chemical Power Sources, College of Chemistry) X Xin Yu Li (State Key Laboratory of Millimeter Wave Southeast University Nanjing China) J Jian Lin Su Q Qiang Xiao Y Yu Ming Ning Z Zhi Cai Yu (State Key Laboratory of Millimeter Wave School of Information Science and Engineering Southeast University Nanjing China) Z Ze Gu J Jie Xu Z Zi Xuan Cai (State Key Laboratory of Millimeter Wave Southeast University Nanjing China) Q Qing Chun Yin (State Key Laboratory of Millimeter Wave School of Information Science and Engineering Southeast University Nanjing China) S Si Qi Huang (State Key Laboratory of Millimeter Wave School of Information Science and Engineering Southeast University Nanjing China) Q Qian Ma (State Key Laboratory of Electroanalytical Chemistry) W Wei Bing Lu (State Key Laboratory of Millimeter Wave School of Information Science and Engineering Southeast University Nanjing China) J Jian Wei You T Tie Jun Cui

Abstract

ABSTRACT Wearable sensors enabling noninvasive healthcare monitoring encounter significant challenges in preserving signal integrity under motion artifacts and mechanical deformation. Here, we present for the first time a wearable sensor that integrates topologically protected flexible metasurface technology, combining topological photonics with AI‐enhanced sensing technology to enable multifunctional human monitoring. This intelligent system harnesses electromagnetic wave‐body interactions to precisely capture cardiopulmonary dynamics, effectively overcoming the limitations of conventional wearable sensors in dynamic conditions. Specifically, the topological design of the sensor ensures stable operational performance even in bent or fractured states, while deep learning algorithms facilitate robust extraction of personalized biometric features to simultaneously achieve multiple healthcare functions, including vital sign monitoring, activity recognition, and individual identification. Experimental results demonstrate the system's capability for real‐time health assessment across diverse scenarios, from exercise to rest states. By combining adaptive wearability with intelligent signal processing, this platform represents a transformative approach to next‐generation smart healthcare systems, advancing applications from chronic disease management to AI‐driven personalized healthcare.

Article Details

Volume / Issue Vol. 38, Issue 10
Published February 01, 2026
ISSN 0935-9648
Publisher Unknown Publisher

Journal Info

Advanced Materials

Unknown Publisher

ISSN: 0935-9648 Physical Sciences

Authors (15)

L

Long Chen

Department of Chemistry, Frontiers Science Center for New Organic Matter and State Key Laboratory of Advanced Chemical Power Sources, College of Chemistry

X

Xin Yu Li

State Key Laboratory of Millimeter Wave Southeast University Nanjing China

J

Jian Lin Su

Q

Qiang Xiao

Y

Yu Ming Ning

Z

Zhi Cai Yu

State Key Laboratory of Millimeter Wave School of Information Science and Engineering Southeast University Nanjing China

Z

Ze Gu

J

Jie Xu

Z

Zi Xuan Cai

State Key Laboratory of Millimeter Wave Southeast University Nanjing China

Q

Qing Chun Yin

State Key Laboratory of Millimeter Wave School of Information Science and Engineering Southeast University Nanjing China

S

Si Qi Huang

State Key Laboratory of Millimeter Wave School of Information Science and Engineering Southeast University Nanjing China

Q

Qian Ma

State Key Laboratory of Electroanalytical Chemistry

W

Wei Bing Lu

State Key Laboratory of Millimeter Wave School of Information Science and Engineering Southeast University Nanjing China

J

Jian Wei You

T

Tie Jun Cui