Current state of digital signal processing in myoelectric interfaces and related applications

被引：222
作者：
Hakonen, Maria [1 ,3 ]
Piitulainen, Harri [2 ]
Visala, Arto [1 ]
机构：
[1] Aalto Univ, Sch Elect Engn, Dept Elect Engn & Automat, Aalto 00076, Finland
[2] Dept Neurosci & Biomed Engn, Brain Res Unit, Espoo 00076, Finland
[3] Aalto Univ, Sch Sci, Dept Neurosci & Biomed Engn, Brain & Mind Lab, Espoo 00076, Finland
来源：
BIOMEDICAL SIGNAL PROCESSING AND CONTROL | 2015年 / 18卷
基金：
芬兰科学院;
关键词：
Surface electromyography; Myoelectric interface; Classification; Feature extraction; Pattern recognition; EMG PATTERN-RECOGNITION; INNERVATION ZONE SHIFT; SURFACE EMG; CLASSIFICATION SCHEME; MUSCULAR FATIGUE; MULTIFUNCTIONAL PROSTHESIS; FEATURE-EXTRACTION; WHEELCHAIR CONTROL; POWER-WHEELCHAIR; MUSCLE SYNERGIES;
D O I：
10.1016/j.bspc.2015.02.009
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
This review discusses the critical issues and recommended practices from the perspective of myoelectric interfaces. The major benefits and challenges of myoelectric interfaces are evaluated. The article aims to fill gaps left by previous reviews and identify avenues for future research. Recommendations are given, for example, for electrode placement, sampling rate, segmentation, and classifiers. Four groups of applications where myoelectric interfaces have been adopted are identified: assistive technology, rehabilitation technology, input devices, and silent speech interfaces. The state-of-the-art applications in each of these groups are presented. (C) 2015 The Authors. Published by Elsevier Ltd.
引用
收藏
页码：334 / 359
页数：26
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