本文選題：表面肌電信號 + 神經(jīng)網(wǎng)絡��； 參考：《東華大學》2017年碩士論文

【摘要】：表面肌電信號(sEMG)是通過電極引導記錄下來的神經(jīng)肌肉系統(tǒng)活動時的生物電信號,能夠?qū)崟r反應肌肉活動狀態(tài),被廣泛應用于助殘、康復醫(yī)學、運動醫(yī)學等領域。由于肌電信號維度很高,在表面肌電信號的模式識別的相關(guān)研究中,需要對數(shù)據(jù)進行降維。本研究采集了7200條6類手部動作的表面肌電信號,用于研究表面肌電信號的降維和動作識別問題。本文從神經(jīng)網(wǎng)絡層數(shù)、隱含層節(jié)點數(shù)、激活函數(shù)、分類模型等方面進行了詳細的討論,在此基礎上構(gòu)建了一個5層神經(jīng)網(wǎng)絡模型對表面肌電信號進行了降維與分類。該模型將維度為3000的原始數(shù)據(jù)作為神經(jīng)網(wǎng)絡的輸入,依次將數(shù)據(jù)從3000維度降維至500維、100維、6維。為了解決高維度輸入下算法溢出問題,在模型最后一層采用6個二元分類器,完成對信號的分類,實驗表明分類準確率達到96.2%。論文使用了第二范數(shù)正則項解決了過擬合的問題,同時詳細說明了如何設定合適的參數(shù)使得算法能夠正常收斂,并達到一個較好的泛化能力。在模型的參數(shù)求解過程中,由于輸入層的數(shù)據(jù)維度較高且存在大量的矩陣運算,導致參數(shù)求解的計算過程過慢。論文提取了參數(shù)求解過程中相同的計算步驟(矩陣點乘與激活函數(shù)運算),運用矩陣運算并行化的思想對樣本數(shù)據(jù)進行分割,將樣本數(shù)據(jù)劃分到相應的計算節(jié)點中,并行運行兩個計算過程,再對各個計算節(jié)點的計算結(jié)果進行歸并,從而加快了計算速度。論文最后對該神經(jīng)網(wǎng)絡模型分層提取特征值的過程進行了詳細分析并利用類間離散度與類內(nèi)離散度這兩個指標對模型分層提取的特征值進行了評估,實驗結(jié)果表明隨著模型層數(shù)的增加,類間離散度與類內(nèi)離散度的比值也不斷增大,這表明該模型提取的特征值是有利于分類的。
[Abstract]:Surface electromyography (SEMG) is a bioelectric signal recorded in the neuromuscular system under the guidance of electrodes, which can respond to the state of muscle activity in real time. It is widely used in the fields of disability, rehabilitation medicine, sports medicine and so on.Because of the high dimension of EMG signal, it is necessary to reduce the dimension of the data in the research of pattern recognition of surface EMG signal.In this study, the surface electromyography (EMG) signals of 7200 hand movements of 6 kinds were collected, which were used to study the demotion and motion recognition of SEMG signals.In this paper, the number of neural network layers, the number of hidden layer nodes, the activation function and the classification model are discussed in detail. On the basis of this, a five-layer neural network model is constructed to reduce the dimension and classify the surface EMG signals.In this model, the original data with dimension 3000 is taken as the input of neural network, and the data is reduced from 3000 dimension to 100D / 100D / 6D in turn.In order to solve the problem of algorithm overflow in high dimensional input, six binary classifiers are used in the last layer of the model to classify the signals. The experimental results show that the classification accuracy reaches 96.22.In this paper, the second norm canonical term is used to solve the problem of over-fitting. At the same time, how to set appropriate parameters to make the algorithm converge normally and achieve a better generalization ability is explained in detail.In the process of solving the parameters of the model, the calculation process of the parameters is too slow because of the high data dimension in the input layer and the existence of a large number of matrix operations.In this paper, the same calculation steps (matrix point multiplication and activation function operation) are extracted in the process of parameter solving, and the sample data is divided into corresponding computing nodes by using the idea of parallelization of matrix operation.Two computing processes are run in parallel, and the results of each computing node are merged, thus speeding up the calculation speed.At the end of the paper, the process of extracting the eigenvalues from the hierarchical model is analyzed in detail, and the eigenvalues extracted from the hierarchical models are evaluated by using the two indexes of inter-class dispersion and intra-class dispersion.The experimental results show that the ratio of inter-class dispersion to intra-class dispersion increases with the increase of the number of model layers, which indicates that the eigenvalues extracted by the model are favorable for classification.
【學位授予單位】：東華大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP183;TN911.7

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