Highly durable machine-learned waterproof electronic glove based on low-cost thermal transfer printing for amphibious wearable applications
作者机构：Joint International Research Laboratory of Information Display and VisualizationSchool of Electronic Science and EngineeringSoutheast UniversityNanjing 210096China Printable Electronics Research CentreSuzhou Institute of Nano-Tech and Nano-BionicsChinese Academy of SciencesSuzhou 215123China
出 版 物：《Nano Research》 (纳米研究（英文版）)
年 卷 期：2023年第16卷第4期
页 面：5480-5489页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0703[理学-化学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0702[理学-物理学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by the National Natural Science Foundation of China(Nos.62075040 and 51603227) the National Key R&D Program of China(No.2017YFE0112000) Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX22_0230)
主 题：data glove transfer printing human-machine interfaces strain sensor amphibious control
摘 要：Gesture recording,modeling,and understanding based on a robust electronic glove(E-glove)are of great significance for efficient human-machine cooperation in harsh ***,such robust edge-intelligence interfaces remain challenging as existing E-gloves are limited in terms of integration,waterproofness,scalability,and interface stability between different ***,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 *** 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 *** 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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