本文選題：肌電信號 + 連續(xù)估計��； 參考：《杭州電子科技大學(xué)》2017年碩士論文

【摘要】：表面肌電信號(Surface Electromyography,sEMG)是肌肉收縮時所產(chǎn)生的動作電位在皮膚表面疊加而成,與肢體的運(yùn)動直接相關(guān),可以用來反應(yīng)人的運(yùn)動意圖。由于sEMG具有蘊(yùn)含信息豐富、采集方便等特點,在運(yùn)動控制、康復(fù)醫(yī)療等領(lǐng)域具有重要應(yīng)用價值。本文以肌電假肢多自由度比例同步控制為立足點,設(shè)計實驗采集上肢關(guān)節(jié)肌肉動作所產(chǎn)生的肌電信號和運(yùn)動參數(shù),結(jié)合肌肉協(xié)同理論從中樞神經(jīng)系統(tǒng)協(xié)同控制人體運(yùn)動的角度出發(fā),著重分析協(xié)同元、激活系數(shù)和角度之間的關(guān)系,并應(yīng)用肌肉協(xié)同的模型解決肌電假肢的比例同步控制問題。首先,相比模式分類控制方法,連續(xù)估計控制方法更符合人體運(yùn)動規(guī)律,可以類人實現(xiàn)關(guān)節(jié)的比例同步控制。本文結(jié)合人體前臂常用動作模式,設(shè)計了上肢三個自由度的獨立和組合動作實驗,通過運(yùn)用三維運(yùn)動捕捉系統(tǒng)同步采集前臂肌電信號和對應(yīng)動作的關(guān)節(jié)角度,為后續(xù)研究打下數(shù)據(jù)基礎(chǔ)。其次,引入肌肉協(xié)同理論來進(jìn)一步解釋和解決人體運(yùn)動過程中多自由度運(yùn)動協(xié)調(diào)問題。通過分而治之的方法確定對應(yīng)前臂六個獨立動作的六個協(xié)同元,利用非負(fù)矩陣分解(Non-negative Matrix Factorization,NMF)算法對肌電信號的均方根特征分解提取出表示中樞神經(jīng)系統(tǒng)低維控制參數(shù)的肌肉協(xié)同元;利用非負(fù)最小二乘算法提取出表示對協(xié)同元刺激程度的激活系數(shù)。通過對比激活系數(shù)和關(guān)節(jié)真實運(yùn)動軌跡,發(fā)現(xiàn)激活系數(shù)可以反映出動作類別和肌肉激活程度,但是激活系數(shù)并不能完全反映出上肢的實際運(yùn)動軌跡,動作之間的激活系數(shù)也沒有充分解耦。然后,為了進(jìn)一步解決激活系數(shù)和運(yùn)動軌跡之間的偏差問題,分解各個動作激活系數(shù)之間的耦合性。通過人工蜂群算法優(yōu)化的支持向量回歸(Support Vector Regression,SVR)算法和BP神經(jīng)網(wǎng)絡(luò)回歸算法分別構(gòu)建了映射激活系數(shù)到關(guān)節(jié)角度的SVR激活模型和BP激活模型,利用建立的激活模型從采集的表面肌電信號得到關(guān)節(jié)運(yùn)動的連續(xù)估計。對兩個關(guān)節(jié)獨立和組合運(yùn)動的估計實驗表明,SVR和BP激活模型均能獲得較好的估計精度,但是SVR激活模型可以獲得更穩(wěn)定和更精確的估計效果。接著,為了分析關(guān)節(jié)動作的快慢程度對SVR激活模型估計效果的影響,設(shè)計了掌關(guān)節(jié)和腕關(guān)節(jié)三個自由度動作分別在高速、中速、低速運(yùn)動模式下的動作實驗。根據(jù)相關(guān)系數(shù)、均方根誤差和t檢驗分析估計結(jié)果,指出SVR激活模型在高速和中速狀態(tài)下可以獲得良好的估計效果。最后,設(shè)計了肌電假肢比例同步控制的在線仿真實驗。通過獨立和組合動作的在線控制任務(wù),測試SVR激活模型對單自由度和多自由度動作的比例同步控制效果。本文研究成果給肌電假肢的控制方法提供了一種新的方案。
[Abstract]:Surface electromyography (EMG) is a superposition of action potential produced by muscle contraction on the skin surface, which is directly related to the movement of the limbs and can be used to reflect the intention of human motion. Because of its rich information and convenient collection, sEMG has important application value in the field of movement control, rehabilitation and medical treatment. In this paper, the multi-degree-of-freedom proportional synchronous control of EMG prosthesis is taken as the foothold, and the EMG signals and motion parameters produced by the muscle movement of the upper limb joint are collected experimentally. Based on the theory of muscle coordination, the relationship among synergetic elements, activation coefficients and angles is analyzed from the point of view of central nervous system coordinated control of human body movement. The model of muscle coordination is applied to solve the problem of proportional synchronous control of myoelectric prosthesis. Firstly, compared with the pattern classification control method, the continuous estimation control method is more consistent with the human motion law, and it can realize the proportional synchronous control of joints. In this paper, combined with the common action mode of human forearm, the experiment of three degrees of freedom of upper limb is designed. The electromyography signal of forearm and the joint angle of corresponding action are collected synchronously by using three-dimensional motion capture system. To lay the data foundation for the follow-up research. Secondly, the theory of muscle coordination is introduced to further explain and solve the problem of multi-degree-of-freedom motion coordination. Using the divide-and-conquer method to determine the six synergetic elements corresponding to the six independent actions of the forearm, Non-negative Matrix factorization (NMF) algorithm is used to extract muscle synergists representing the low dimensional control parameters of central nervous system (CNS). The non-negative least squares algorithm is used to extract the activation coefficients representing the stimulus degree of the synergists. By comparing the activation coefficient with the real motion trajectory of the joint, it was found that the activation coefficient could reflect the type of movement and the degree of muscle activation, but the activation coefficient could not completely reflect the actual movement track of the upper limb. The activation coefficients between actions are also not fully decoupled. Then, in order to solve the problem of the deviation between the activation coefficient and the motion trajectory, the coupling between the activation coefficients of each action is decomposed. The SVR activation model and BP activation model of mapping activation coefficient to joint angle are constructed by the support vector regression support Vector regression algorithm and BP neural network regression algorithm optimized by artificial bee colony algorithm, respectively. The continuous estimation of joint motion is obtained from the collected surface EMG signals using the established activation model. The experiments of joint independent and combined motion estimation show that both SVR and BP activation model can obtain better estimation accuracy, but SVR activation model can obtain more stable and accurate estimation results. Then, in order to analyze the influence of the speed and slowness of joint action on the estimation effect of SVR activation model, three degrees of freedom (DOF) motions of metacarpal joint and wrist joint were designed in high speed, middle speed and low speed motion mode respectively. According to the correlation coefficient, root mean square error and t test, it is pointed out that the SVR activation model can obtain good estimation results at high and medium speed. Finally, the online simulation experiment of proportion synchronous control of myoelectric prosthesis is designed. Through the on-line control task of independent and combined actions, the proportional synchronization control effect of SVR activation model for single and multi-degree-of-freedom actions is tested. The results of this paper provide a new scheme for the control of myoelectric prosthesis.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R318.17;TN911.7

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