Emerging Wearable Interfaces and Algorithms for Hand Gesture Recognition: A Survey

IF 17.2 1区 工程技术 Q1 ENGINEERING, BIOMEDICAL IEEE Reviews in Biomedical Engineering Pub Date : 2021-03-07 DOI:10.1109/RBME.2021.3078190
Shuo Jiang;Peiqi Kang;Xinyu Song;Benny P.L. Lo;Peter B. Shull
{"title":"Emerging Wearable Interfaces and Algorithms for Hand Gesture Recognition: A Survey","authors":"Shuo Jiang;Peiqi Kang;Xinyu Song;Benny P.L. Lo;Peter B. Shull","doi":"10.1109/RBME.2021.3078190","DOIUrl":null,"url":null,"abstract":"Hands are vital in a wide range of fundamental daily activities, and neurological diseases that impede hand function can significantly affect quality of life. Wearable hand gesture interfaces hold promise to restore and assist hand function and to enhance human-human and human-computer communication. The purpose of this review is to synthesize current novel sensing interfaces and algorithms for hand gesture recognition, and the scope of applications covers rehabilitation, prosthesis control, exoskeletons for augmentation, sign language recognition, human-computer interaction, and user authentication. Results showed that electrical, mechanical, acoustical/vibratory, and optical sensing were the primary input modalities in gesture recognition interfaces. Two categories of algorithms were identified: 1) classification algorithms for predefined, fixed hand poses and 2) regression algorithms for continuous finger and wrist joint angles. Conventional machine learning algorithms, including linear discriminant analysis, support vector machines, random forests, and non-negative matrix factorization, have been widely used for a variety of gesture recognition applications, and deep learning algorithms have more recently been applied to further facilitate the complex relationship between sensor signals and multi-articulated hand postures. Future research should focus on increasing recognition accuracy with larger hand gesture datasets, improving reliability and robustness for daily use outside of the laboratory, and developing softer, less obtrusive interfaces.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"15 ","pages":"85-102"},"PeriodicalIF":17.2000,"publicationDate":"2021-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/RBME.2021.3078190","citationCount":"56","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Reviews in Biomedical Engineering","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/9426433/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 56

Abstract

Hands are vital in a wide range of fundamental daily activities, and neurological diseases that impede hand function can significantly affect quality of life. Wearable hand gesture interfaces hold promise to restore and assist hand function and to enhance human-human and human-computer communication. The purpose of this review is to synthesize current novel sensing interfaces and algorithms for hand gesture recognition, and the scope of applications covers rehabilitation, prosthesis control, exoskeletons for augmentation, sign language recognition, human-computer interaction, and user authentication. Results showed that electrical, mechanical, acoustical/vibratory, and optical sensing were the primary input modalities in gesture recognition interfaces. Two categories of algorithms were identified: 1) classification algorithms for predefined, fixed hand poses and 2) regression algorithms for continuous finger and wrist joint angles. Conventional machine learning algorithms, including linear discriminant analysis, support vector machines, random forests, and non-negative matrix factorization, have been widely used for a variety of gesture recognition applications, and deep learning algorithms have more recently been applied to further facilitate the complex relationship between sensor signals and multi-articulated hand postures. Future research should focus on increasing recognition accuracy with larger hand gesture datasets, improving reliability and robustness for daily use outside of the laboratory, and developing softer, less obtrusive interfaces.
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
新兴的手势识别可穿戴界面和算法综述
手在广泛的基本日常活动中至关重要,阻碍手功能的神经系统疾病会严重影响生活质量。可穿戴手势界面有望恢复和辅助手部功能,增强人机交流。这篇综述的目的是综合当前用于手势识别的新型传感接口和算法,应用范围包括康复、假体控制、用于增强的外骨骼、手语识别、人机交互和用户身份验证。结果表明,电气、机械、声学/振动和光学传感是手势识别界面中的主要输入模式。确定了两类算法:1)预定义的固定手姿势的分类算法和2)连续手指和手腕关节角度的回归算法。传统的机器学习算法,包括线性判别分析、支持向量机、随机森林和非负矩阵因子分解,已被广泛用于各种手势识别应用,最近,深度学习算法被应用于进一步促进传感器信号和多关节手姿势之间的复杂关系。未来的研究应侧重于通过更大的手势数据集提高识别精度,提高实验室外日常使用的可靠性和稳健性,并开发更柔软、不那么突兀的界面。
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
IEEE Reviews in Biomedical Engineering
IEEE Reviews in Biomedical Engineering Engineering-Biomedical Engineering
CiteScore
31.70
自引率
0.60%
发文量
93
期刊介绍: IEEE Reviews in Biomedical Engineering (RBME) serves as a platform to review the state-of-the-art and trends in the interdisciplinary field of biomedical engineering, which encompasses engineering, life sciences, and medicine. The journal aims to consolidate research and reviews for members of all IEEE societies interested in biomedical engineering. Recognizing the demand for comprehensive reviews among authors of various IEEE journals, RBME addresses this need by receiving, reviewing, and publishing scholarly works under one umbrella. It covers a broad spectrum, from historical to modern developments in biomedical engineering and the integration of technologies from various IEEE societies into the life sciences and medicine.
期刊最新文献
Foundation Model for Advancing Healthcare: Challenges, Opportunities and Future Directions. A Manual for Genome and Transcriptome Engineering. Artificial General Intelligence for Medical Imaging Analysis. A Survey of Few-Shot Learning for Biomedical Time Series. The Physiome Project and Digital Twins.
×
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