總體經驗模組分解應用於表面肌電訊號之疲勞分析

Using Ensemble Empirical Mode Decomposition for Fatigue Analysis of Surface Electromyogram

吳宣漢(Xuan-Han Wu)

指導教授 ： 劉省宏

朝陽科技大學/資訊學院/資訊工程系/碩士(2011年)

摘要

肌肉耐力是不可或缺的基本體適能，也是人們身體健康的重要指標之一。本研究目的是想了解當運動時，透過所測量到的肌電訊號，其中位頻率變化，來呈現肌肉疲勞現象。研究中透過十名健康的學生（五位男生及五位女生）為受試者， 以大腿的骨外側肌為例，每人皆接受連續1個月運動測量，並透過自製擷取裝置，擷錄肌電訊號並加以分析，分析的方法是針對原始的肌電訊號，和利用三種訊號分解技術，小波分解、經驗模組分解與總體經驗模組分解，尋找肌電訊號中最佳的展頻訊號，其對於肌肉疲勞之呈現有最佳的靈敏度與穩定性。結果顯示肌電訊號透過EEMD的分解，其本質模組函數1，最能夠描述肌肉疲勞的現象。

關鍵字

肌電訊號 ； 中位頻率 ； 肌肉疲勞 ； 本質模組函數 ； 總體經驗模組分解

並列摘要

Muscular endurance is not only an indispensable part of physical fitness, but also an important indicator for people’s health. The purpose of this study was to detect the muscle fatigue with the change of the median frequency of the electromygram (EMG) signal measured in exercise. Ten healthy students (five males and five females) were selected as subjects. All of them went a one month sports. A self designed device was used to measure and analytics the EMG signal of vastus lateralis. Three signal decomposition technologies, Wavelet Decomposition, Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD), were used to find the optimum spectrum band that had the most sensitivity and stability for the phenomenon of muscle fatigue. The results show that the intrinsic mode function (IMF) 1 of EEMD has the best performance.

並列關鍵字

EMG ； median frequency ； Ensemble Empirical Mode Decomposition ； intrinsic mode function ； muscle fatigue

參考文獻

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