本文選題：肌電信號 + 模式識別��； 參考：《哈爾濱工業(yè)大學》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、手臂姿勢變化、肌肉收縮力變化、個體差異、長期時變等,從而導致實際應用中肌電控制的成功率較低。針對上述干擾因素,本文分別從特征提取方法、分類器泛化能力、自適應學習策略等方面進行研究。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(Support Vector Machine,SVM)核函數(shù)的學習策略,基于代表樣本更新的在線無監(jiān)督學習策略等。本文首先綜述了國內(nèi)外肌電模式識別的研究現(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、手臂姿勢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)驗選擇的方法,簡化了過零點數(shù)(Zero Crossing,ZC)、脈沖百分率(Myopulse Percentage Rate,MYOP)、Willison幅值(Willison Amplitude,WAMP)、斜率符號變化(Slope Sign Change,SSC)等特征的參數(shù)選擇過程,識別正確率平均提升10.2%;此外,本文提出將絕對均值(Mean Absolute Value,MAV)、均方根(Root Mean Square,RMS)等傳統(tǒng)常用特征與DFT、WT、WPT相結合的復合式特征提取方法,該方法能夠明顯提高肌電模式識別的魯棒性,分別將識別正確率提升30.5%、25.4%、22.9%。針對優(yōu)勢手/非優(yōu)勢手互換、手臂姿勢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行研究。首先引入概率神經(jīng)網(wǎng)絡(Probabilistic Neural Networks,PNN)作為肌電模式識別的分類器,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。然后研究了SVM的核函數(shù),提出一種多核學習的方式,以提升SVM的泛化能力。實驗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別成功率,相比于高斯核,成功率平均提升了1.5%。針對長期時變、手臂姿勢變化等混雜因素導致的模式識別成功率下降問題,本文提出一種基于代表樣本的在線學習策略,能夠從訓練集中選擇最能代表類別信息的樣本。實驗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別成功率的退化,也能提升電極位置竄動、肌肉收縮力變化等更復雜因素干擾下的識別成功率。
[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.

【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R496;TP391.4

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本文編號：1835687