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

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.

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Publication Details

Journal
Advanced Materials
Published
2026-04-16
DOI
https://doi.org/10.1002/adma.73094
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Junyi Zhai, Aifang Yu, Qichang Hu, Shidai Tian et al.
Advanced Materials
Advanced Sensor and Energy Harvesting Materials
article

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

Junyi Zhai, Aifang Yu, Qichang Hu, Shidai Tian, Di Guo, Yulong Wang, Wei Wang, Zhong Lin Wang
article en

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.

Advanced Materials
University of Electronic Science and Technology of China (CN), Guangxi University (CN), Guangxi Science and Technology Department (CN), Beijing Institute of Nanoenergy and Nanosystems (CN), University of Chinese Academy of Sciences (CN), Fujian Agriculture and Forestry University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 16%
Advanced Sensor and Energy Harvesting Materials
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