Surface Electromyography Signal Processing and Classification Techniques

被引：606

作者：

Chowdhury, Rubana H. ^{[1
]}

Reaz, Mamun B. I. ^{[1
]}

Ali, Mohd Alauddin Bin Mohd ^{[1
]}

Bakar, Ashrif A. A. ^{[1
]}

Chellappan, Kalaivani ^{[1
]}

Chang, Tae. G. ^{[2
]}

机构：

[1] Univ Kebangsaan Malaysia, Dept Elect Elect & Syst Engn, Bangi 43600, Selangor, Malaysia

[2] Chung Ang Univ, Sch Elect & Elect Engn, Seoul 156756, South Korea

来源：

SENSORS | 2013年 / 13卷 / 09期

关键词：

electromyography; noise source; wavelet; EMD; ICA; artificial neural network; HOS; INDEPENDENT COMPONENT ANALYSIS; EMPIRICAL MODE DECOMPOSITION; EMG PATTERN-RECOGNITION; WAVELET TRANSFORM; NEURAL-NETWORK; FEATURE-PROJECTION; MOTION ARTIFACT; MUSCLE FATIGUE; NOISE; SYSTEM;

D O I：

10.3390/s130912431

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.

引用

页码：12431 / 12466

页数：36

共 112 条

[21] THE WAVELET TRANSFORM, TIME-FREQUENCY LOCALIZATION AND SIGNAL ANALYSIS [J].

DAUBECHIES, I .

IEEE TRANSACTIONS ON INFORMATION THEORY, 1990, 36 (05) :961-1005

[22] Man-Machine Interface System for Neuromuscular Training and Evaluation Based on EMG and MMG Signals [J].

de la Rosa, Ramon ;

Alonso, Alonso ;

Carrera, Albano ;

Duran, Ramon ;

Fernandez, Patricia .

SENSORS, 2010, 10 (12) :11100-11125

[23]

De Luca C.J., 1988, ENCYCLOPAEDIA MEDICA, P1111

[24]

DELBOCA A, 1994, 1994 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS, VOL 1-7, P3098, DOI 10.1109/ICNN.1994.374728

[25]

Djupsjbacka M., 1999, Modern Techniques in Neuroscience Research, P705, DOI [10.1007/978-3-642-58552-426, DOI 10.1007/978-3-642-58552-4_26, DOI 10.1007/978-3-642-58552-426]

[26] Classification of the myoelectric signal using time-frequency based representations [J].

Engelhart, K ;

Hudgins, B ;

Parker, PA ;

Stevenson, M .

MEDICAL ENGINEERING & PHYSICS, 1999, 21 (6-7) :431-438

[27] A robust, real-time control scheme for multifunction myoelectric control [J].

Englehart, K ;

Hudgins, B .

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2003, 50 (07) :848-854

[28] A wavelet-based continuous classification scheme for multifunction myoelectric control [J].

Englehart, K ;

Hudgins, B ;

Parker, PA .

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2001, 48 (03) :302-311

[29]

Englehart K, 2000, INTELLIGENT SYSTEMS

[30]

Fahlman S. E., 1990, P ADV NEUR INF PROC

← 1 2 3 4 5 6 7 8 9 10 →