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    Real-time identification of electromyographic signals from hand movement
    (2012-10-02)
    Sappat, Assawapong
    ;
    Mahaphonchaikul, Kritsanaphan
    ;
    Sangworasil, Manas
    ;
    Pintavirooj, Chuchat
    ;
    Tuantranont, Adisorn
    In this work, we have demonstrated a novel on-line technology for real-time acquisition and identification of electromyographic (EMG) signals from hand movement. EMG signal were measured using standard surface electrodes from forearm muscles at three major points including Wrist extensor, Flexor Carpi Radialis and Wrist Flexor groups, respectively. The EMG acquisition system consists of an instrumentation amplifier, filter circuit, isolator, an amplifier with gain adjustment and a commercial embedded system called FiO board. The commercial FiO embedded system is interfaced with the computer and EMG is represented, analyzed and stored in real-time on computer by Simulink program. EMG signals are identified by RMS and SD feature extraction methods and k-mean clustering algorithm. The result revealed that both RMS and SD can be used with k-mean clustering algorithm to obtain the distinct Euclidean distance characteristic of EMG signal for each movement. The minimum Euclidean distance with RMS and SD for each hand movement uniquely occurs at a distinct Euclidean distances between real EMG data and extracted features. © 2012 IEEE.
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    Comparison study of muscular-contraction classification between independent component analysis and artificial neural network
    (2008-12-01)
    Sueaseenak, Direk
    ;
    Wibirama, Sunu
    ;
    Chanwimalueang, Theerasak
    ;
    Pintavirooj, Chuchart
    ;
    Sangworasil, Manus
    We developed a multi-channel electromyogram acquisition system using PSOC microcontroller to acquire multichannel EMG signals. An array of 4 x 4 surface electrodes was used to record the EMG signal. The obtained signals were classified by a back-propagation-type artificial neural network. B-spline interpolation technique has been utilized to map the EMG signal on the muscle surface. The topological mapping of the EMG is then analyzed to classify the pattern of muscle contraction using independent component analysis. The proposed system was successfully demonstrated to record EMG data and its surface mapping. The comparison study of muscular contraction classification using independent component analysis and artificial neural network demonstrates shows that performance of ANN classification is as comparable as that of the ICA. The computational time of ANN is also less than that of the ICA. © 2008 IEEE.
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    Robotic arm controller using muscular contraction classification based on independent component analysis
    (2008-10-06)
    Chanwimalueang, T.
    ;
    Sueaseenak, D.
    ;
    Laoopugsin, N.
    ;
    Pintavirooj, C.
    We develop a multi-channel electromyogram acquisition system base on the Programmable System On Chip (PSOC) microcontroller to control Robotic Arm. The array of 4 × 4 surface electrodes which invents from the low-cost EKG electrodes is used as the input sensor. B-spline interpolation technique has been utilized to map the EMG signal on the muscle surface. The topological mapping of the EMG is then analyzed to classify the pattern of muscle contraction. The proposed system was successfully demonstrated to record EMG data and its surface mapping. The muscular-contraction classification of mapping is then applied using independent component analysis. The classification result is then applied to control the movement of the robotic arm. © 2008 IEEE.
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    Independent component analysis: An application for muscular contraction classification
    (2007-12-01)
    Daochai, S.
    ;
    Sueaseenak, D.
    ;
    Chanwimalueang, T.
    ;
    Laoopugsin, N.
    ;
    Pintavirooj, C.
    We develop a multi-channel electromyogram acquisition system using PSOC microcontroller to aquire multichannel EMG signals. An array of 4 x 4 surface electrodes is used to record the EMG signal. B-spline interpolation technique has been utilized to map the EMG signal on the muscle surface. The topological mapping of the EMG is then analyzed to classify the pattern of muscle contraction. The proposed system was successfully demonstrated to record EMG data and its surface mapping. The muscular contraction classification of mapping using independent component analysis demonstrates promising results. ©2007 IEEE.