EM Wave-Based Hand Gesture Recognition for Astronauts Using 3D Memristive Neural Network

IF 6.9 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Shilpa Pavithran;Sruthi Pallathuvalappil;Elizabeth George;Javed G S;Alex James
{"title":"EM Wave-Based Hand Gesture Recognition for Astronauts Using 3D Memristive Neural Network","authors":"Shilpa Pavithran;Sruthi Pallathuvalappil;Elizabeth George;Javed G S;Alex James","doi":"10.1109/JMW.2024.3506736","DOIUrl":null,"url":null,"abstract":"The astronauts' spacesuit helmet is generally fitted with a communications carrier assembly (CCA), which has a critical role in ensuring the safety of the astronauts by enabling clear communication during spacewalks. While on spacewalks, often hand gestures are used to communicate between crew members. In this paper, to automatically recognize the hand gestures, the classification of electromagnetic (EM) waves from a patch antenna placed on the hand of an astronaut is performed using a three-dimensional memristive Artificial Neural Network (3D-ANN). Performance characteristics of Ku-band microstrip patch antennas on glass, PET (Polyethylene terephthalate), and FR4 (Flame retardant-4) substrates are analyzed in this work. In the case of FR4 and glass substrate, copper is deposited as the patch, while graphene is deposited as the patch on the PET substrate. The work is proposed for the space suite of astronauts as an alternative for communications carrier assembly (CCA), and hence simulations and experiments are performed for standalone antenna, standalone antenna on Body model, ON-Body to ON-Body, and ON-Body to OFF-Body scenarios. Four hand gestures are performed and classified using a three-dimensional memristive Artificial Neural Network (3D-ANN) based on Skywater 130 nm PDK (SKY130) for the ON-body to OFF-body scenario with an accuracy of 80%. Variability analysis is also performed in the 3D-ANN classifier.","PeriodicalId":93296,"journal":{"name":"IEEE journal of microwaves","volume":"5 1","pages":"48-58"},"PeriodicalIF":6.9000,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10799157","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE journal of microwaves","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10799157/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

The astronauts' spacesuit helmet is generally fitted with a communications carrier assembly (CCA), which has a critical role in ensuring the safety of the astronauts by enabling clear communication during spacewalks. While on spacewalks, often hand gestures are used to communicate between crew members. In this paper, to automatically recognize the hand gestures, the classification of electromagnetic (EM) waves from a patch antenna placed on the hand of an astronaut is performed using a three-dimensional memristive Artificial Neural Network (3D-ANN). Performance characteristics of Ku-band microstrip patch antennas on glass, PET (Polyethylene terephthalate), and FR4 (Flame retardant-4) substrates are analyzed in this work. In the case of FR4 and glass substrate, copper is deposited as the patch, while graphene is deposited as the patch on the PET substrate. The work is proposed for the space suite of astronauts as an alternative for communications carrier assembly (CCA), and hence simulations and experiments are performed for standalone antenna, standalone antenna on Body model, ON-Body to ON-Body, and ON-Body to OFF-Body scenarios. Four hand gestures are performed and classified using a three-dimensional memristive Artificial Neural Network (3D-ANN) based on Skywater 130 nm PDK (SKY130) for the ON-body to OFF-body scenario with an accuracy of 80%. Variability analysis is also performed in the 3D-ANN classifier.
基于三维记忆神经网络的航天员电磁波手势识别
航天员宇航服头盔一般配备通信载体组件(CCA),在航天员太空行走过程中实现清晰的通信,对保障航天员安全起着至关重要的作用。在太空行走时,宇航员之间经常使用手势进行交流。为了实现手势的自动识别,本文采用三维记忆人工神经网络(3D-ANN)对宇航员手上贴片天线发出的电磁波进行分类。本文分析了玻璃、PET(聚对苯二甲酸乙二醇酯)和FR4(阻燃剂-4)基板上ku波段微带贴片天线的性能特性。在FR4和玻璃基板的情况下,铜作为贴片沉积,而石墨烯作为贴片沉积在PET基板上。这项工作是为宇航员的空间套件提出的,作为通信载波组件(CCA)的替代方案,因此对独立天线、独立天线在身体模型上、on -Body到on -Body以及on -Body到OFF-Body场景进行了模拟和实验。使用基于Skywater 130 nm PDK (SKY130)的三维记忆性人工神经网络(3D-ANN)执行四种手势并对其进行分类,准确率为80%。在3D-ANN分类器中也进行了变异性分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
10.70
自引率
0.00%
发文量
0
审稿时长
8 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信