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<p>Augmented-tactility wearable devices have attracted significant attention for their potential to expand the boundaries of human tactile capabilities and their broad applications in medical rehabilitation. Nonetheless, these devices face challenges in practical applications, including high susceptibility to the operating environments, such as variations in pressure, humidity, and touch speed, as well as concerns regarding wearability and comfort. In this work, we developed an augmented-tactility superskin, termed AtSkin, which integrates a skin-compatible nanofiber sensor array and deep learning algorithms to enhance material recognition regardless of the ambient environment. We fabricated a lightweight and breathable triboelectric sensor array with multilayer nanofiber architectures through electrospinning and hot pressing. The carefully selected combination of sensing layers can capture the electrical characteristics of different materials, thus enabling their distinction. Combined with deep learning algorithms, AtSkin achieved an accuracy of 97.9% in distinguishing visually similar resin and fabric materials,&#xa0;even under varying environmental pressures and humidities.&#xa0;As a proof of concept, we constructed an intelligent augmented-tactility system&#xa0;capable of identifying fabrics with similar textures and hand feel,&#xa0;demonstrating the potential of the superskin to expand human tactile capabilities,&#xa0;enhance augmented reality experiences, and revolutionize intelligent healthcare solutions.</p> Graphical Abstract <p></p>

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Nanofiber-Based Superskin for Augmented Tactility

  • 期刊论文

  • 发表日期：

    2025年4月28日

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  • Mengjia Zhu,
  • Shuo Li,
  • Peng Bi,
  • Huarun Liang,
  • Xun-En Wu,
  • Chi Zhang,
  • Xian Song,
  • Aifang Yu,
  • Jingtao Xu,
  • Haojie Lu,
  • Haomin Wang,
  • Junyi Zhai,
  • Yi Li,
  • Zijian Zheng,
  • Yingying Zhang

摘要

Augmented-tactility wearable devices have attracted significant attention for their potential to expand the boundaries of human tactile capabilities and their broad applications in medical rehabilitation. Nonetheless, these devices face challenges in practical applications, including high susceptibility to the operating environments, such as variations in pressure, humidity, and touch speed, as well as concerns regarding wearability and comfort. In this work, we developed an augmented-tactility superskin, termed AtSkin, which integrates a skin-compatible nanofiber sensor array and deep learning algorithms to enhance material recognition regardless of the ambient environment. We fabricated a lightweight and breathable triboelectric sensor array with multilayer nanofiber architectures through electrospinning and hot pressing. The carefully selected combination of sensing layers can capture the electrical characteristics of different materials, thus enabling their distinction. Combined with deep learning algorithms, AtSkin achieved an accuracy of 97.9% in distinguishing visually similar resin and fabric materials, even under varying environmental pressures and humidities. As a proof of concept, we constructed an intelligent augmented-tactility system capable of identifying fabrics with similar textures and hand feel, demonstrating the potential of the superskin to expand human tactile capabilities, enhance augmented reality experiences, and revolutionize intelligent healthcare solutions.

Graphical Abstract