A High-Humidity Resistance Flexible Tribovoltaic Smart Textile for Sign Language and Athletic Status Recognition

IF 22.6 1区工程技术 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY

Advanced Fiber Materials Pub Date : 2026-02-12 DOI:10.1007/s42765-025-00674-y

Ruifei Luan, Guoxu Liu, Zhi Zhang, Liang Qiao, Jie Cao, Ziyue Wang, Feiling Luo, Beibei Fan, Likun Gong, Yuan Feng, Chi Zhang

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引用次数: 0

Abstract

Smart textiles demonstrate transformative potential for robotics and wearable applications, but their operational stability remains critically dependent on environmental conditions. Herein, we proposed a high-humidity resistance flexible knitted tribovoltaic smart textile (TST), which was prepared of Al wire and poly (3,4-ethylenedioxythiophene): poly (styrene sulfonate), and polyethylene glycol-based conductive cotton. Compared to traditional triboelectric textile that suffer from severe electrical output degradation or even signal failure in humid environments, the TST achieves advancement in electromechanical stability at high humidity. Under extreme conditions (85% RH), the open-circuit voltage and short-circuit current of TST are 0.88 V and 8.12 μA, exhibiting exceptional moisture resistance. Attaching to human joints (wrists, knees, fingers, etc.), the TST can effectively monitor the movement state of the human body. By integrating it with machine learning technology, the recognition and sensing of sign language gestures can be realized, whose recognition accuracy of eight different sign language gestures can reach 98.75%. This work demonstrates the application potential of tribovoltaic wearable electronic skin in fields such as motion monitoring and robotics.

Graphical Abstract

The alternative text for this image may have been generated using AI.

The application and prospect of flexible smart textile in the fields of wearable devices and E-skin.

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一种用于手语和运动员身份识别的高湿耐柔性摩擦伏智能纺织品

智能纺织品展示了机器人和可穿戴应用的变革潜力，但其运行稳定性仍然严重依赖于环境条件。在此，我们提出了一种高湿耐挠性针织摩擦伏打智能纺织品（TST），它由铝丝和聚（3,4-乙烯二氧噻吩）、聚（苯乙烯磺酸盐）和聚乙二醇基导电棉制备而成。传统的摩擦电纺织品在潮湿环境中遭受严重的电输出退化甚至信号失效，相比之下，TST在高湿环境下实现了机电稳定性的进步。在极端条件下（85% RH）， TST的开路电压为0.88 V，短路电流为8.12 μA，具有良好的耐湿性。TST附着在人体关节（手腕、膝盖、手指等）上，可以有效地监测人体的运动状态。将其与机器学习技术相结合，实现对手语手势的识别与感知，对8种不同的手语手势的识别准确率可达98.75%。这项工作展示了摩擦伏打可穿戴电子皮肤在运动监测和机器人等领域的应用潜力。此图像的替代文本可能是使用AI生成的。柔性智能纺织品在可穿戴设备和电子皮肤领域的应用与前景。

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来源期刊

Advanced Fiber Materials Multiple-

CiteScore

18.70

自引率

11.20%

发文量

109

期刊介绍： Advanced Fiber Materials is a hybrid, peer-reviewed, international and interdisciplinary research journal which aims to publish the most important papers in fibers and fiber-related devices as well as their applications.Indexed by SCIE, EI, Scopus et al. Publishing on fiber or fiber-related materials, technology, engineering and application.