@article{Duan2023, 
author = {Shengshun Duan and Jiayi Wang and Yong Lin and Jianlong Hong and Yucheng Lin and Yier Xia and Yinghui Li and Di Zhu and Wei Lei and Wenming Su and Baoping Wang and Zheng Cui and Wei Yuan and Jun Wu},
title = {Highly durable machine-learned waterproof electronic glove based on low-cost thermal transfer printing for amphibious wearable applications},
year = {2023},
journal = {Nano Research},
volume = {16},
number = {4},
pages = {5480-5489},
keywords = {transfer printing, strain sensor, human–machine interfaces, data glove, amphibious control},
url = {https://www.sciopen.com/article/10.1007/s12274-022-5077-9},
doi = {10.1007/s12274-022-5077-9},
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.}
}