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Author: search.filters.author.Sueaseenak, D.

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    EMG signal feature extraction based on Wavelet transform
    (2010-07-30)
    Mahaphonchaikul, K.
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    Sueaseenak, D.
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    Pintavirooj, C.
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    Sangworasil, M.
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    Tungjitkusolmun, S.
    In this paper, a multi-channel electromyogram acquisition system using programmable system on chip (PSOC) microcontroller was used to obtain the surface of EMG signal. Two pairs of single-channel surface electrodes were used to measure and record the EMG signal on forearm muscles. Then, different levels of Daubechies Wavelet family were performed to analyze the EMG signal. Finally, features in terms of root mean square, logarithm of root mean square, centroid of frequency, and standard deviation were used to extract the EMG signal. The experimental results show that root mean square feature extraction method exhibits better performance for extracting the EMG signal compared to the other features. In the future, our method can be utilized to control a mechanical arm in real-time processing.
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    Robotic arm controller using muscular contraction classification based on independent component analysis
    (2008-10-06)
    Chanwimalueang, T.
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    Sueaseenak, D.
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    Laoopugsin, N.
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    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.