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Research Article

Highly durable machine-learned waterproof electronic glove based on low-cost thermal transfer printing for amphibious wearable applications

Shengshun Duan1,§, Jiayi Wang2,§, Yong Lin2, Jianlong Hong1, Yucheng Lin1, Yier Xia1, Yinghui Li1, Di Zhu1, Wei Lei1, Wenming Su2, Baoping Wang1, Zheng Cui2, Wei Yuan2(), Jun Wu1 ()
Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, China
Printable Electronics Research Centre, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, China

§ Shengshun Duan and Jiayi Wang contributed equally to this work.

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Graphical Abstract

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A system-level waterproof washable electronic glove (E-glove) is reported, which is of low cost, light weight, and scalable for mass production, assisted by an improved neural network architecture, which implements environment-adaptive learning and inference for hand gesture with 100% accuracy. The amphibious remote vehicle navigation via hand gestures is also demonstrated.

Abstract

Gesture recording, modeling, and understanding based on a robust electronic glove (E-glove) are of great significance for efficient human-machine cooperation in harsh environments. However, such robust edge-intelligence interfaces remain challenging as existing E-gloves are limited in terms of integration, waterproofness, scalability, and interface stability between different components. Here, we report on the design, manufacturing, and application scenarios for a waterproof E-glove, which is of low cost, lightweight, and scalable for mass production, as well as environmental robustness, waterproofness, and washability. An improved neural network architecture is proposed to implement environment-adaptive learning and inference for hand gestures, which achieves an amphibious recognition accuracy of 100% in 26 categories by analyzing 2,600 hand gesture patterns. We demonstrate that the E-glove can be used for amphibious remote vehicle navigation via hand gestures, potentially opening the way for efficient human-human and human-machine cooperation in harsh environments.

Keywords

data glovetransfer printinghuman–machine interfacesstrain sensoramphibious control

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Nano Research
Volume 16 Issue 4,
April 2023
Pages 5480-5489
DOI: 10.1007/s12274-022-5077-9
Cite this article:
Duan S, Wang J, Lin Y, et al. Highly durable machine-learned waterproof electronic glove based on low-cost thermal transfer printing for amphibious wearable applications. Nano Research, 2023, 16(4): 5480-5489. https://doi.org/10.1007/s12274-022-5077-9
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