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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/26147


    Title: 經驗模組分解函數應用於關節硬化與肌肉疲勞之肌電訊號參數萃取研究
    Authors: 張剛鳴
    Contributors: 資訊學院;光電與通訊學系
    Date: 2012
    Issue Date: 2013-07-18 15:34:41 (UTC+8)
    Abstract: 表面肌電訊號(sEMG)分析主要有傳統線性分析的時域、頻域與進一步的非線性分析方法。除了傳統的線性與非線性訊號分析方法之外,近年來經驗模組分解(Empirical Mode Decomposition,EMD)則是備受關注的訊號分析方法。 EMD是基於希爾伯特-黃轉換(Hilbert-Huang Transformation以下簡稱HHT)內的演算法。這是一個高效率、可自變與方便使用的時變程序演算法。主要使用於非線性與非穩態之訊號分析。此外。總體經驗模組分解(Ensemble Empirical Mode Decomposition,以下簡稱EEMD)的引入也改善了EMD的模組函數混合效應 ,增加了之前以EMD計算所得參數效果 。本研究計畫以退化性膝關節炎的患者使用乳油木果油前後之肌電訊號為分析範本。討論以EMD/EEMD 求出的sEMG參數,比對傳統Fourier transform 以及wavelet 分解所推算出的特徵參數,對於應用於關節硬化與肌肉疲勞的優越性。其中將分別從時域參數的RMS amplitude、頻域參數的Median frequency、非線性參數則以遞歸量化分析(Recurrence Quantification Analysis,RQA)的%RET及 % DET著手比較。目前初步結果顯示EEMD計算的Median frequency參數比其他方法的肌肉疲勞變化斜率更為明顯。

    There are three major feature extraction domain of surface EMG (sEMG), time domain, frequency domain and nonlinear domain. Recently, Empirical Mode Decomposition (EMD) is developed. EMD is based on Hilbert-Huang transform (HHT) algorithm. This is mainly used in non-linear and non-steady-state signal analysis.. In addition, Ensemble Empirical Mode Decomposition (EEMD) is improvement of EMD by reducing mode mixing effects on EMD. This project investigates the feature performance derived by EMD and EEMD, for three feature extraction domain on sEMG. That is RMS amplitude, median frequency, and %RET / %DET derived from Recurrence Quantification Analysis. Pilot study showed that Median frequency derived from EEMD has lowest slope variation than the other method, such as Fourier transform, wavelet and EMD.
    Appears in Collections:[光電與通訊學系] 科技部研究計畫

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