Wrist motion pattern recognition system by EMG signals

被引：0
|
作者：
Matsumura, Y [1 ]
Fukurni, M [1 ]
Akamatsu, N [1 ]
机构：
[1] Univ Tokushima, Fac Engn, Tokushima 7708506, Japan
来源：
KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 1, PROCEEDINGS | 2005年 / 3681卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we aim for construction of high-speed and high-accurate system using Fast Fourier Transform (FFT) for feature extraction, Simple-PCA (SPCA) for feature compression, and a neural network (NN) for recognition. In particular, we present a novel method based on Canonical Discriminant Analysis (CDA) to improve recognition accuracy for EMG. From results of computer simulation, it is shown that our approach is effective for improvement in recognition accuracy.
引用
收藏
页码：611 / 617
页数：7
相关论文
共 50 条
  • [31] COMPUTER-ASSISTED PROCESSING OF EMG SIGNALS BY PATTERN-RECOGNITION METHOD
    MOLNAR, E
    IVANYOS, L
    TOTH, J
    ILLYES, S
    ELECTROENCEPHALOGRAPHY AND CLINICAL NEUROPHYSIOLOGY, 1977, 42 (06): : 856 - 856
  • [32] Surface EMG signals based motion intent recognition using multilayer ELM
    Wang, Jianhui
    Qi, Lin
    Wang, Xiao
    LIDAR IMAGING DETECTION AND TARGET RECOGNITION 2017, 2017, 10605
  • [33] Comparison of advanced control techniques for motion intention recognition using EMG signals
    Sulez Gomez, Felipe Antonio
    Ruiz Guacheta, William Alejandro
    Guzman Villamarin, Diego Enrique
    Londono Prieto, Jeronimo
    Diago Rodriguez, Juan Pablo
    2017 IEEE 3RD COLOMBIAN CONFERENCE ON AUTOMATIC CONTROL (CCAC), 2017,
  • [34] Surface EMG signals pattern recognition utilizing an adaptive crosstalk suppression preprocessor
    Nazarpour, K.
    2005 ICSC CONGRESS ON COMPUTATIONAL INTELLIGENCE METHODS AND APPLICATIONS (CIMA 2005), 2005, : 159 - 161
  • [35] Conditioning and Sampling Issues of EMG Signals in Motion Recognition of Multifunctional Myoelectric Prostheses
    Guanglin Li
    Yaonan Li
    Long Yu
    Yanjuan Geng
    Annals of Biomedical Engineering, 2011, 39 : 1779 - 1787
  • [36] Real-time intelligent pattern recognition algorithm for surface EMG signals
    Khezri, Mahdi
    Jahed, Mehran
    BIOMEDICAL ENGINEERING ONLINE, 2007, 6 (1)
  • [37] Pattern recognition of electromyography (EMG) signal for wrist movement using learning vector quantization (LVQ)
    Putra, D. S.
    Weru, Y. U. W.
    Fitriady
    1ST SOUTH ACEH INTERNATIONAL CONFERENCE ON ENGINEERING AND TECHNOLOGY (SAICOET), 2019, 506
  • [38] Personal Authentication and Hand Motion Recognition based on Wrist EMG Analysis by a Convolutional Neural Network
    Shioji, Ryohei
    Ito, Shin-ichi
    Ito, Momoyo
    Fukumi, Minoru
    2018 IEEE INTERNATIONAL CONFERENCE ON INTERNET OF THINGS AND INTELLIGENCE SYSTEM (IOTAIS), 2018, : 184 - 188
  • [39] Personal Authentication and Hand Motion Recognition based on Wrist EMG Analysis by a Convolutional Neural Network
    Shioji, Ryohei
    Ito, Shin-ichi
    Ito, Momoyo
    Fukumi, Minoru
    2018 JOINT 10TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS (SCIS) AND 19TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS (ISIS), 2018, : 1172 - 1176
  • [40] Wrist EMG Signals Identification using Neural Network
    Oyama, Tadahiro
    Karungaru, Stephen Githinji
    Tsuge, Satoru
    Fukumi, Minoru
    Mitsukura, Yasue
    IECON: 2009 35TH ANNUAL CONFERENCE OF IEEE INDUSTRIAL ELECTRONICS, VOLS 1-6, 2009, : 4073 - +
12345