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  5. Ultra‐Robust and Hyperelastic Triboelectric Webbing for Self‐Powered Rehabilitation Sensing with Invisible and Embedded Design

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Article
en
2026

Ultra‐Robust and Hyperelastic Triboelectric Webbing for Self‐Powered Rehabilitation Sensing with Invisible and Embedded Design

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en
2026
DOI: 10.1002/adma.73094

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Wei Wang
Yulong Wang
Di Guo
+6 more

Abstract

Driven by the rapid evolution of flexible electronics, rehabilitation healthcare is shifting toward devices that seamlessly interface with human body. Yet, existing solutions often simply layer flexible sensor units over rigid components, making it difficult to combine high elasticity, mechanical robustness, and true imperceptibility. Here, we are pioneering a super-tough (∼54.7 MPa) and highly stretchable (>400% strain) triboelectric webbing (T-webbing) that overcomes this long-standing trade-off through the synergistic integration of an embedded textured architecture and functional elastic yarns. The T-webbing supports mass customization, exhibits outstanding electrical durability (>100 000 cycles), and enables reliable self-powered sensing capability with tunable mechanical properties for diverse rehabilitation tasks. In a proof-of-concept demonstration, the T-webbing is seamlessly integrated into a machine-learning-enabled lower-limb rehabilitation platform, achieving a motion recognition accuracy of 97.9% while enabling seamless one-click data sharing, intuitive human-machine interaction, and real-time remote guidance. By bridging high mechanical resilience with imperceptible wearability, our study offers a brand-new solution for data-driven, high-compliance, home-based rehabilitation within the Internet-of-Things ecosystem-addressing a pressing clinical need for scalable, patient-friendly solutions.

How to cite this publication

Wei Wang, Yulong Wang, Di Guo, Shidai Tian, Shuhui Wang, Qichang Hu, Aifang Yu, Zhong Lin Wang, Junyi Zhai (2026). Ultra‐Robust and Hyperelastic Triboelectric Webbing for Self‐Powered Rehabilitation Sensing with Invisible and Embedded Design. , DOI: https://doi.org/10.1002/adma.73094.

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Publication Details

Type

Article

Year

2026

Authors

9

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1002/adma.73094

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