本文關(guān)鍵詞： 表面肌電信號(hào) 小波包變換 支持向量機(jī) 模式識(shí)別　出處：《吉林大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：手臂動(dòng)作表面肌電信號(hào)是利用表面電極從手臂皮膚表面記錄下來的肌肉電信號(hào)，它可以量化的反映手臂進(jìn)行動(dòng)作時(shí)神經(jīng)以及肌肉的功能狀態(tài)。由于表面肌電信號(hào)的提取方式具有方便、準(zhǔn)確、無創(chuàng)傷等優(yōu)點(diǎn)，在康復(fù)醫(yī)學(xué)、運(yùn)動(dòng)醫(yī)學(xué)及智能機(jī)器人等領(lǐng)域都有廣泛的研究與應(yīng)用。隨著信息科學(xué)技術(shù)的不斷發(fā)展，準(zhǔn)確的從表面肌電信號(hào)中提取有效特征，依據(jù)信號(hào)特征實(shí)現(xiàn)高分辨的動(dòng)作模式識(shí)別，成為肌電信號(hào)控制仿生假肢技術(shù)的關(guān)鍵所在。本文依托吉林省科技發(fā)展計(jì)劃重點(diǎn)項(xiàng)目具有溫度、觸滑覺臨場感的仿生手臂研制與開發(fā)(批準(zhǔn)號(hào)：20090350)，實(shí)施對手臂動(dòng)作表面肌電信號(hào)模式分類方法的研究，以促進(jìn)項(xiàng)目中肌電控制仿生假肢的實(shí)用化，因此本文的研究具有重要的科研價(jià)值與社會(huì)意義。 本文的主要工作有： 1.明確表面肌電信號(hào)特點(diǎn)，結(jié)合局部解剖學(xué)相關(guān)知識(shí)，明確對手臂進(jìn)行動(dòng)作時(shí)貢獻(xiàn)最大的兩塊肌肉，并確定電極采取信號(hào)的有利位置，利用貼片電極與肌電信號(hào)采集儀器完成了手臂常見動(dòng)作模式的表面肌電信號(hào)的提取工作。 2.利用時(shí)域分析法、頻域分析法及時(shí)頻域分析法對所采集到的表面肌電信號(hào)進(jìn)行了特征提取工作，分析數(shù)據(jù)結(jié)果，認(rèn)為時(shí)域分析法與頻域分析法具有片面性，確定由典型的時(shí)頻域分析法的小波包方法來提取表面肌電信號(hào)的特征，最終由小波包系數(shù)的方差與能量作為特征元素組成特征向量。 3.分析了模式識(shí)別的主要方法，確定由標(biāo)準(zhǔn)支持向量機(jī)，最小二乘支持向量機(jī)，BP神經(jīng)網(wǎng)絡(luò)算法對所得到的特征向量進(jìn)行模式識(shí)別。進(jìn)行了參數(shù)選擇方法的討論與實(shí)驗(yàn)，分析了三種參數(shù)尋優(yōu)方法的特點(diǎn)。 4.對三種模式識(shí)別算法的正確識(shí)別率及訓(xùn)練時(shí)間進(jìn)行了統(tǒng)計(jì)分析，確定了應(yīng)用粒子群方法進(jìn)行參數(shù)尋優(yōu)，，最小二乘支持向量機(jī)進(jìn)行模式識(shí)別的優(yōu)越性，其具有較高的識(shí)別率和較短的運(yùn)算時(shí)間。雖然參數(shù)尋優(yōu)的運(yùn)算時(shí)間較長，但是分類器模型的訓(xùn)練與測試可分為兩步，所以將參數(shù)尋優(yōu)放入分類器模型的訓(xùn)練步驟中，利用獲得的分類器模型進(jìn)行動(dòng)作模式識(shí)別的時(shí)間就可以大大縮短。 5.利用Matlab的GUI模塊對手臂動(dòng)作模式離線識(shí)別系統(tǒng)的開發(fā)，對表面肌電信號(hào)的各個(gè)處理環(huán)節(jié)進(jìn)行整合，使系統(tǒng)變得可視化、易操作。
[Abstract]:The electromyography (EMG) signal of the arm movement surface is recorded from the skin surface of the arm using the surface electrode. It can quantificationally reflect the functional state of the nerve and muscle when the arm is moving. Because of the convenience, accuracy and no trauma of the surface EMG signal extraction, it is in rehabilitation medicine. Sports medicine and intelligent robot are widely studied and applied. With the development of information science and technology, accurate extraction of effective features from surface EMG signals. High resolution motion pattern recognition based on signal features has become the key of EMG control bionic prosthesis technology. This paper relies on the key project of Jilin province science and technology development plan has temperature. The research and development of the biomimetic arm of touch and slip telepresence (approval No.: 20090350) is carried out to study the classification method of electromyography (EMG) signals on the surface of the arm. In order to promote the utility of electromyoelectric control of biomimetic prosthesis in the project, the research of this paper has important scientific research value and social significance. The main work of this paper is as follows: 1. Make clear the characteristics of surface EMG signal, combined with the relevant knowledge of local anatomy, identify the two muscles that contribute the most to the arm movement, and determine the favorable position of the electrode signal. The surface electromyography (EMG) signal extraction of the common arm action mode is accomplished by using patch electrode and EMG signal acquisition instrument. 2. The time domain analysis and the frequency domain analysis are used to extract the features of the collected surface EMG signals, and the results are analyzed. The time-domain analysis and frequency-domain analysis are considered to be one-sidedness. The wavelet packet method of time-frequency domain analysis is used to extract the features of surface EMG signal. Finally, the eigenvector is composed of the variance and energy of wavelet packet coefficients as characteristic elements. 3. The main methods of pattern recognition are analyzed and determined by standard support vector machine and least square support vector machine. BP neural network algorithm for pattern recognition of the obtained eigenvector, parameter selection methods are discussed and experimental, and the characteristics of three parameter optimization methods are analyzed. 4. The correct recognition rate and training time of the three pattern recognition algorithms are statistically analyzed, and the superiority of using particle swarm optimization method and least square support vector machine for pattern recognition is determined. Although the operation time of parameter optimization is longer, the training and testing of classifier model can be divided into two steps. So the time of action pattern recognition using the obtained classifier model can be greatly shortened by putting the parameter optimization into the training step of the classifier model. 5. Using the GUI module of Matlab to develop the off-line recognition system of arm action pattern, and integrate all the processing links of the surface EMG signal, so that the system becomes visual and easy to operate.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.7

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 于育民;梁瑛;;MATLAB-GUI在圖形可視化中的應(yīng)用[J];安陽工學(xué)院學(xué)報(bào);2010年04期

2 加玉濤;羅志增;;肌電信號(hào)特征提取方法綜述[J];電子器件;2007年01期

3 史海成;王春艷;張媛媛;;淺談模式識(shí)別[J];今日科苑;2007年22期

4 鄭逢德;張鴻賓;;Lagrange雙支撐向量回歸機(jī)[J];計(jì)算機(jī)科學(xué);2011年12期

5 熊金志;胡金蓮;袁華強(qiáng);;光滑支持向量機(jī)的原理和進(jìn)展[J];計(jì)算機(jī)工程;2008年13期

6 劉祥樓;賈東旭;李輝;姜繼玉;;說話人識(shí)別中支持向量機(jī)核函數(shù)參數(shù)優(yōu)化研究[J];科學(xué)技術(shù)與工程;2010年07期

7 全生寅;;Kuhn-Tucker條件與線性規(guī)劃的對偶性[J];數(shù)學(xué)的實(shí)踐與認(rèn)識(shí);2006年01期

8 劉東平;單甘霖;張岐龍;段修生;;基于改進(jìn)遺傳算法的支持向量機(jī)參數(shù)優(yōu)化[J];微計(jì)算機(jī)應(yīng)用;2010年05期

9 徐勛華,王繼成;支撐向量機(jī)的多類分類方法[J];微電子學(xué)與計(jì)算機(jī);2004年10期

10 王剛;王志中;胡曉;黃永峰;;基于最佳小波包的表面肌電信號(hào)分類方法[J];中國醫(yī)學(xué)物理學(xué)雜志;2006年01期

本文編號(hào)：1442636