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.
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.