本文選題：肌電信號(hào) + 模式識(shí)別��； 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：肌電控制受到諸多因素的干擾,如電極位置竄動(dòng)、手臂姿勢(shì)變化、肌肉收縮力變化、個(gè)體差異、長期時(shí)變等,從而導(dǎo)致實(shí)際應(yīng)用中肌電控制的成功率較低。針對(duì)上述干擾因素,本文分別從特征提取方法、分類器泛化能力、自適應(yīng)學(xué)習(xí)策略等方面進(jìn)行研究。主要內(nèi)容包括:基于粒子群優(yōu)化(Particle Swarm Optimization,PSO)算法的特征閾值優(yōu)化方法,基于離散傅里葉變換(Discrete Fourier Transform,DFT)、小波變換(Wavelet Transform,WT)及小波包變換(Wavelet Packet Transform,WPT)的特征提取方法,基于支持向量機(jī)(Support Vector Machine,SVM)核函數(shù)的學(xué)習(xí)策略,基于代表樣本更新的在線無監(jiān)督學(xué)習(xí)策略等。本文首先綜述了國內(nèi)外肌電模式識(shí)別的研究現(xiàn)狀,發(fā)現(xiàn)了目前的研究所存在的一些問題,并確定本文的主要研究內(nèi)容。為了減少電極位置竄動(dòng)、手臂姿勢(shì)及肌肉收縮力變化等混雜因素的干擾,本文首先從肌電模式特征提取方面進(jìn)行研究。提出一種基于PSO算法的特征閾值優(yōu)化方法,相比于傳統(tǒng)的基于經(jīng)驗(yàn)選擇的方法,簡化了過零點(diǎn)數(shù)(Zero Crossing,ZC)、脈沖百分率(Myopulse Percentage Rate,MYOP)、Willison幅值(Willison Amplitude,WAMP)、斜率符號(hào)變化(Slope Sign Change,SSC)等特征的參數(shù)選擇過程,識(shí)別正確率平均提升10.2%;此外,本文提出將絕對(duì)均值(Mean Absolute Value,MAV)、均方根(Root Mean Square,RMS)等傳統(tǒng)常用特征與DFT、WT、WPT相結(jié)合的復(fù)合式特征提取方法,該方法能夠明顯提高肌電模式識(shí)別的魯棒性,分別將識(shí)別正確率提升30.5%、25.4%、22.9%。針對(duì)優(yōu)勢(shì)手/非優(yōu)勢(shì)手互換、手臂姿勢(shì)及肌肉收縮力變化等混雜因素的干擾,本文從提升分類器的泛化能力方面進(jìn)行研究。首先引入概率神經(jīng)網(wǎng)絡(luò)(Probabilistic Neural Networks,PNN)作為肌電模式識(shí)別的分類器,發(fā)現(xiàn)其泛化能力比線性判別分析分類器(Linear Discriminant Analysis,LDA)更強(qiáng)。然后研究了SVM的核函數(shù),提出一種多核學(xué)習(xí)的方式,以提升SVM的泛化能力。實(shí)驗(yàn)證明基于高斯核的多尺度核函數(shù)能夠取得最高的模式識(shí)別成功率,相比于高斯核,成功率平均提升了1.5%。針對(duì)長期時(shí)變、手臂姿勢(shì)變化等混雜因素導(dǎo)致的模式識(shí)別成功率下降問題,本文提出一種基于代表樣本的在線學(xué)習(xí)策略,能夠從訓(xùn)練集中選擇最能代表類別信息的樣本。實(shí)驗(yàn)證明該方法不僅能夠緩解電極長期佩戴過程中肌電模式識(shí)別成功率的退化,也能提升電極位置竄動(dòng)、肌肉收縮力變化等更復(fù)雜因素干擾下的識(shí)別成功率。
[Abstract]:The EMG control is disturbed by many factors, such as the movement of the electrode position, the change of arm posture, the change of muscle contractile force, the individual difference, the long time change and so on, which leads to the low success rate of the electromyography control in the practical application. The main contents include: the feature threshold optimization method based on Particle Swarm Optimization (PSO) algorithm, the feature extraction method based on discrete Fourier transform (Discrete Fourier Transform, DFT), wavelet transform (Wavelet Transform, WT) and wavelet packet transform (Wavelet), based on support The learning strategy of the Support Vector Machine (SVM) kernel function is based on the online unsupervised learning strategy, which represents the update of the sample. This paper first summarizes the research status of the EMG pattern recognition at home and abroad, and finds some problems in the present research, and determines the main contents of this paper. In this paper, a new method of feature threshold optimization based on PSO algorithm is proposed. Compared with the traditional method based on experiential selection, the number of zero crossing points (Zero Crossing, ZC) and pulse percentage (Myopulse Percentage) are simplified. Rate, MYOP), Willison amplitude (Willison Amplitude, WAMP), slope symbol change (Slope Sign Change, SSC) and other characteristics of the parameter selection process, the recognition accuracy is improved by an average of 10.2%. Furthermore, this paper puts forward the combination of the traditional common features such as absolute mean (Mean Absolute), mean square root and other common features. Combined feature extraction method, this method can obviously improve the robustness of EMG pattern recognition. The recognition accuracy is increased by 30.5%, 25.4%, 22.9%. for the interference of the mixed factors such as the hand / non dominant hand exchange, the arm posture and the changes of the muscle contractile force. This paper first introduces the generalization ability of the lifting classifier. Probabilistic Neural Networks (PNN), as a classifier for EMG pattern recognition, finds that its generalization ability is stronger than that of linear discriminant analysis classifier (Linear Discriminant Analysis, LDA). Then, the kernel function of SVM is studied and a multi kernel learning method is proposed to improve the generalization ability of SVM. The experiment is based on Gauss. The kernel's multi-scale kernel function can achieve the highest success rate of pattern recognition. Compared with the Gauss kernel, the success rate increases the success rate of pattern recognition in 1.5%. for long time variation and arm posture change. This paper proposes an online learning strategy based on representative sample, which can choose the most from the training center. The experiment shows that the method can not only alleviate the degradation of the success rate of the electromyographic pattern recognition during the long-term wear of the electrode, but also improve the recognition success rate under the interference of more complex factors such as the change of the electrode position and the changes of the muscle contractile force.

【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R496;TP391.4

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本文編號(hào)：1835687