Hand Gesture Recognition Using Convolutional Neural Network for People Who Have Experienced A Stroke

Norah Alnaim, M. Abbod, Abdulrahman Albar
{"title":"Hand Gesture Recognition Using Convolutional Neural Network for People Who Have Experienced A Stroke","authors":"Norah Alnaim, M. Abbod, Abdulrahman Albar","doi":"10.1109/ISMSIT.2019.8932739","DOIUrl":null,"url":null,"abstract":"A human gesture is a non-verbal form of communication and is critical in human-robot interactions. Vision-based gesture recognition methods play a key role to detect hand motion and support such interactions. Hand gesture recognition allows a appropriate, and usable interface between users and devices. Hand gestures can be used for various fields which makes it be able to be implemented for communication and further. Hand gesture recognition is not only useful for people who are hearing-impaired or disabled but also for the people who have experienced a stroke, as they need to communicate with other people using different common essential gestures such as the sign of eating, drink, family and, more. In this paper, a system for recognizing hand gesture based on Convolutional Neural Network (CNN) is proposed. The developed method is evaluated and compared between training and testing modes based on several metrics such as execution time, accuracy, sensitivity, specificity, positive and negative predictive value, likelihood and root mean square. Results show that testing accuracy is 99% using CNN and is an effective technique in extracting distinct features and classifying data.","PeriodicalId":169791,"journal":{"name":"2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISMSIT.2019.8932739","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12

Abstract

A human gesture is a non-verbal form of communication and is critical in human-robot interactions. Vision-based gesture recognition methods play a key role to detect hand motion and support such interactions. Hand gesture recognition allows a appropriate, and usable interface between users and devices. Hand gestures can be used for various fields which makes it be able to be implemented for communication and further. Hand gesture recognition is not only useful for people who are hearing-impaired or disabled but also for the people who have experienced a stroke, as they need to communicate with other people using different common essential gestures such as the sign of eating, drink, family and, more. In this paper, a system for recognizing hand gesture based on Convolutional Neural Network (CNN) is proposed. The developed method is evaluated and compared between training and testing modes based on several metrics such as execution time, accuracy, sensitivity, specificity, positive and negative predictive value, likelihood and root mean square. Results show that testing accuracy is 99% using CNN and is an effective technique in extracting distinct features and classifying data.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
使用卷积神经网络识别中风患者的手势
人类手势是一种非语言的交流形式,在人机交互中至关重要。基于视觉的手势识别方法在检测手部运动和支持这种交互方面起着关键作用。手势识别允许在用户和设备之间提供适当的、可用的界面。手势可以用于各种领域,这使得它能够实现通信和进一步。手势识别不仅对听力受损或残疾的人有用,而且对中风患者也有用,因为他们需要使用不同的常见基本手势与他人交流,如吃饭、喝水、家庭等。本文提出了一种基于卷积神经网络(CNN)的手势识别系统。基于执行时间、准确性、灵敏度、特异性、阳性预测值和阴性预测值、似然值和均方根等指标,对训练模式和测试模式进行了评价和比较。结果表明,该方法的测试准确率可达99%,是一种有效的特征提取和分类技术。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Machine Learning Applications in Disease Surveillance Open-Source Web-Based Software for Performing Permutation Tests Graph-Based Representation of Customer Reviews for Online Stores Aynı Şartlar Altında Farklı Üretici Çekişmeli Ağların Karşılaştırılması Keratinocyte Carcinoma Detection via Convolutional Neural Networks
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1