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.