The use of Artificial Neural Network in the Classification of EMG Signals

被引：11

作者：

Ahsan, Md. R. ^{[1
]}

Ibrahimy, Muhammad I. ^{[1
]}

Khalifa, Othman O. ^{[1
]}

机构：

[1] Int Islamic Univ Malaysia, Fac Engn, Dept Elect & Comp Engn, Kuala Lumpur 53100, Malaysia

来源：

2012 THIRD FTRA INTERNATIONAL CONFERENCE ON MOBILE, UBIQUITOUS, AND INTELLIGENT COMPUTING (MUSIC) | 2012年

关键词：

Electromyography; Artificial Neural Network; Back-Propagation; Levenberg-Marquardt algorithm; EMG Signal Classifier etc; PATTERN-RECOGNITION;

D O I：

10.1109/MUSIC.2012.46

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents the design, optimization and performance evaluation of artificial neural network for the efficient classification of Electromyography (EMG) signals. The EMG signals are collected for different types of volunteer hand motion which are processed to extract some predefined features as inputs to the neural network. The time and time-frequency based extracted feature sets are used to train the neural network. A back-propagation neural network with Levenberg-Marquardt training algorithm has been employed for the classification of EMG signals. The results show that the designed and optimized network able to classify single channel EMG signals with an average success rate of 88.4%.

引用

页码：225 / 229

页数：5

共 50 条

[41] Rowing stroke force estimation with EMG signals using artificial neural networks
Mobasser, F
Hashtrudi-Zaad, K
2005 IEEE INTERNATIONAL CONFERENCE ON CONTROL APPLICATIONS (CCA), VOLS 1AND 2, 2005, : 825 - 830
[42] Detection of Knee Abnormality from Surface EMG Signals by Artificial Neural Networks
Erkaymaz, Okan
Senyer, Irem
Uzun, Rukiye
2017 25TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2017,
[43] Discrimination of EMG Signals Using a Neuromorphic Implementation of a Spiking Neural Network
Donati, Elisa
Payvand, Melika
Risi, Nicoletta
Krause, Renate
Indiveri, Giacomo
IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, 2019, 13 (05) : 795 - 803
[44] A multi-level feature fusion artificial neural network for classification of acoustic emission signals
Huang, Jinliang
Zhu, Zhaolin
Chen, Zhihao
Lu, Haotian
Yang, Zijin
ANNALS OF THE NEW YORK ACADEMY OF SCIENCES, 2025, 1544 (01) : 223 - 241
[45] Application of empirical mode decomposition and artificial neural network for the classification of normal and epileptic EEG signals
Djemili, Rafik
Bourouba, Hocine
Korba, M. C. Amara
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING, 2016, 36 (01) : 285 - 291
[46] Temporal generalization of an artificial neural network for land use/land cover classification
Tolentino, Franciele M.
Galo, Maria de Lourdes B. T.
Christovam, Luiz E.
Coladello, Leandro F.
EARTH RESOURCES AND ENVIRONMENTAL REMOTE SENSING/GIS APPLICATIONS IX, 2018, 10790
[47] Classification of Respiratory Pathology in Pulmonary Acoustic Signals Using Parametric Features and Artificial Neural Network
Palaniappan, Rajkumar
Sundaraj, Kenneth
Sundaraj, Sebastian
Huliraj, N.
Revadi, S. S.
Archana, B.
2014 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND COMPUTING RESEARCH (IEEE ICCIC), 2014, : 134 - 139
[48] K-means clustering analysis and artificial neural network classification of fatigue strain signals
Yunoh, M. F. M.
Abdullah, S.
Saad, M. H. M.
Nopiah, Z. M.
Nuawi, M. Z.
JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING, 2017, 39 (03) : 757 - 764
[49] Artificial Neural Network for Laparoscopic Skills Classification Using Motion Signals from Apple Watch
Laverde, Rubbermaid
Rueda, Claudia
Amado, Lusvin
Rojas, David
Altuve, Miguel
2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2018, : 5434 - 5437
[50] Feature Selection and Classification of Electroencephalographic Signals: An Artificial Neural Network and Genetic Algorithm Based Approach
Erguzel, Turker Tekin
Ozekes, Serhat
Tan, Oguz
Gultekin, Selahattin
CLINICAL EEG AND NEUROSCIENCE, 2015, 46 (04) : 321 - 326

← 1 2 3 4 5 →