A Review of Hand Gesture Recognition Systems Based on Noninvasive Wearable Sensors

被引:24
|
作者
Tchantchane, Rayane [1 ]
Zhou, Hao [1 ]
Zhang, Shen [1 ]
Alici, Gursel [1 ]
机构
[1] Univ Wollongong, Sch Mech Mat Mechatron & Biomed Engn, Appl Mechatron & Biomed Engn Res AMBER Grp, Wollongong, NSW 2522, Australia
关键词
classifications; deep learning; hand gesture recognitions; noninvasive sensors; upper-limb wearables; SIGN-LANGUAGE; MYOELECTRIC CONTROL; EMG; TIME; REHABILITATION; FOREARM; FORCE; GLOVE; WRIST; MMG;
D O I
10.1002/aisy.202300207
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Hand gesture, one of the essential ways for a human to convey information and express intuitive intention, has a significant degree of differentiation, substantial flexibility, and high robustness of information transmission to make hand gesture recognition (HGR) one of the research hotspots in the fields of human-human and human-computer or human-machine interactions. Noninvasive, on-body sensors can monitor, track, and recognize hand gestures for various applications such as sign language recognition, rehabilitation, myoelectric control for prosthetic hands and human-machine interface (HMI), and many other applications. This article systematically reviews recent achievements from noninvasive upper-limb sensing techniques for HGR, multimodal sensing fusion to gain additional user information, and wearable gesture recognition algorithms to obtain more reliable and robust performance. Research challenges, progress, and emerging opportunities for sensor-based HGR systems are also analyzed to provide perspectives for future research and progress.
引用
收藏
页数:19
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