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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 Lin2Jianlong Hong1Yucheng Lin1Yier Xia1Yinghui Li1Di Zhu1Wei Lei1Wenming Su2Baoping Wang1Zheng Cui2Wei 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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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.

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Nano Research
Pages 5480-5489
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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