Presentation 2014-10-19
EMG motion classification based on Wavelet transformation
Takayuki MUKAEDA, Nan BU,
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Abstract(in English) Studies of motion classification using EMG signal are has been performed in many cases. Recently, study utilizing frequency features of EMG signals are performed. We extracted frequency features from EMG signals using short-time Fourier transform, and occurred forearm motion classification. However, classification becomes unstable by switching part of the operation for improving temporal resolution is difficult. Therefore, we focused on Wavelet transform (WT) which can be solve the problem of temporal resolution. In this study, the frequency features are extracted by WT, and these feature patterns are then inputted into a multi-layer perceptron (MLP) neural network in order to classify forearm motions. As a result, classification rates of the proposed method were used for compare with EMG motion classification method of our previous study using STFT. From this result, the validity of the proposed method was confirmed.
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Keyword(in English) EMG signal / frequency features / Wavelet transform / pattern classification / neural network / forearm motion
Paper # WIT2014-45
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Committee WIT
Conference Date 2014/10/12(1days)
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Registration To Well-being Information Technology(WIT)
Language JPN
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Title (in English) EMG motion classification based on Wavelet transformation
Sub Title (in English)
Keyword(1) EMG signal
Keyword(2) frequency features
Keyword(3) Wavelet transform
Keyword(4) pattern classification
Keyword(5) neural network
Keyword(6) forearm motion
1st Author's Name Takayuki MUKAEDA
1st Author's Affiliation NIT, Kumamoto College()
2nd Author's Name Nan BU
2nd Author's Affiliation NIT, Kumamoto College
Date 2014-10-19
Paper # WIT2014-45
Volume (vol) vol.114
Number (no) 261
Page pp.pp.-
#Pages 5
Date of Issue