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
收藏
页码：225 / 229
页数：5
相关论文
共 50 条
  • [21] Classification of Encephalographic Signals using Artificial Neural Networks
    Sepulveda, Roberto
    Montiel, Oscar
    Diaz, Gerardo
    Gutierrez, Daniel
    Castillo, Oscar
    COMPUTACION Y SISTEMAS, 2015, 19 (01): : 69 - 88
  • [22] 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
  • [23] An analysis of the performance of Artificial Neural Network technique for apple classification
    Bhatt A.K.
    Pant D.
    Singh R.
    AI & SOCIETY, 2014, 29 (1) : 103 - 111
  • [24] Question classification for E-learning by artificial neural network
    Fei, T
    Heng, WJ
    Toh, KC
    Qi, T
    ICICS-PCM 2003, VOLS 1-3, PROCEEDINGS, 2003, : 1757 - 1761
  • [25] Prediction of joint moments using a neural network model of muscle activations from EMG signals
    Wang, L
    Buchanan, TS
    IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2002, 10 (01) : 30 - 37
  • [26] Classification of a Driver's cognitive workload levels using artificial neural network on ECG signals
    Tjolleng, Amir
    Jung, Kihyo
    Hong, Wongi
    Lee, Wonsup
    Lee, Baekhee
    You, Heecheon
    Son, Joonwoo
    Park, Seikwon
    APPLIED ERGONOMICS, 2017, 59 : 326 - 332
  • [27] K-means clustering analysis and artificial neural network classification of fatigue strain signals
    M. F. M. Yunoh
    S. Abdullah
    M. H. M. Saad
    Z. M. Nopiah
    M. Z. Nuawi
    Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2017, 39 : 757 - 764
  • [28] ARTIFICIAL NEURAL NETWORKS APPLIED TO THE CLASSIFICATION OF HAND GESTURES USING ELETROMYOGRAPHIC SIGNALS
    Fonseca, M. G. B.
    Conceicao, A. G. S.
    Simas Filho, E. F.
    2017 LATIN AMERICAN ROBOTICS SYMPOSIUM (LARS) AND 2017 BRAZILIAN SYMPOSIUM ON ROBOTICS (SBR), 2017,
  • [29] Classification of coffee using artificial neural network
    Yip, DHF
    Yu, WWH
    1996 IEEE INTERNATIONAL CONFERENCE ON EVOLUTIONARY COMPUTATION (ICEC '96), PROCEEDINGS OF, 1996, : 655 - 658
  • [30] Artificial Neural-Network EMG Classifier for Hand Movements Prediction
    Gandolla, M.
    Ferrante, S.
    Baldassini, D.
    Cottini, M. Cotti
    Seneci, C.
    Pedrocchi, A.
    XIV MEDITERRANEAN CONFERENCE ON MEDICAL AND BIOLOGICAL ENGINEERING AND COMPUTING 2016, 2016, 57 : 634 - 637
12345