【摘要】：表面肌電(surface Electromyography, sEMG)信號是一種復(fù)雜的人體表皮下肌肉電活動(dòng)在皮膚表面處時(shí)間和空間上的綜合結(jié)果,是從人體骨骼肌表面通過非侵入方式記錄下來的神經(jīng)肌肉活動(dòng)時(shí)發(fā)放的生物電信號,它能在非損傷狀態(tài)下實(shí)時(shí)反映神經(jīng)肌肉的功能狀態(tài)。本文主要研究人體下肢sEMG信號的采集與處理以及基于sEMG信號的運(yùn)動(dòng)模式辨識方法,研究內(nèi)容主要涉及神經(jīng)—肌肉學(xué)科中的神經(jīng)肌電信號、信號處理和模式識別等方面。 隨著材料、傳感器和計(jì)算機(jī)等技術(shù)發(fā)展,國內(nèi)外對表面肌電的研究也逐步深入,使得表面肌電信號不僅在運(yùn)動(dòng)醫(yī)學(xué)、臨床醫(yī)學(xué)及康復(fù)醫(yī)學(xué)等領(lǐng)域被廣泛應(yīng)用,而且還成為了人工假肢的理想控制信號。sEMG信號的模式識別是其應(yīng)用的基礎(chǔ),為此,本文深入探討了如何由采集的sEMG信號來識別下肢不同的運(yùn)動(dòng)模式,其目的就是基于sEMG信號的非平穩(wěn)性和隨機(jī)性,運(yùn)用現(xiàn)代信號處理方法尋求其內(nèi)在的本質(zhì)特征,并深入研究及運(yùn)用現(xiàn)代模式識別理論設(shè)計(jì)模式分類器,使其能夠?qū)ο轮\(yùn)動(dòng)模式的本征值進(jìn)行有效識別,為揭示sEMG信號的本質(zhì)與多自由度肌電控制假肢的實(shí)用化提供理論依據(jù),主要工作和創(chuàng)新之處如下： 1.設(shè)計(jì)了sEMG信號的放大濾波電路,sEMG信號的放大濾波電路是實(shí)現(xiàn)肌電信號采集系統(tǒng)的關(guān)鍵,根據(jù)sEMG信號的幅頻特性和外界信號對其影響,本文設(shè)計(jì)了較好的濾波電路,特別是設(shè)計(jì)出了一種新型的50Hz工頻陷波電路,能夠很好地解決工頻噪聲對sEMG信號的不良影響； 2.利用小波變換的多分辨率分析技術(shù),利用小波分解系數(shù)矩陣的奇異值構(gòu)建sEMG信號特征向量,結(jié)合BP神經(jīng)網(wǎng)絡(luò)和支持向量機(jī)分類器,對人體下肢常見六種運(yùn)動(dòng)的分類進(jìn)行了研究,并完成了對下肢肌肉的疲勞評估和基于sEMG信號的路況識別； 3.提出了基于粒子群優(yōu)化的過程神經(jīng)網(wǎng)絡(luò)分類算法,兼顧了采集信號的空間耦合作用和時(shí)間積累效應(yīng),避免了因特征提取而導(dǎo)致的信息流失,在無需進(jìn)行特征提取的情況下,完成了對人體下肢不同運(yùn)動(dòng)模式的自動(dòng)分類。與傳統(tǒng)的過程神經(jīng)網(wǎng)絡(luò)算法相比,本文采用的粒子群算法通過對網(wǎng)絡(luò)系數(shù)的優(yōu)化,大大提高了算法的執(zhí)行效率,并取得了理想的分類準(zhǔn)確率。
[Abstract]:Surface electromyography (surface Electromyography, sEMG) signal is a complex subepidermal muscle electrical activity on the surface of the skin time and space synthesis results. It is a bioelectric signal which is recorded from the surface of human skeletal muscle in a non-invasive manner when the neuromuscular activity is recorded. It can reflect the functional state of the neuromuscular in a non-invasive state in real time. This paper mainly studies the acquisition and processing of human lower limb sEMG signal and the method of motion pattern identification based on sEMG signal. The research content mainly involves neuromyogram signal, signal processing and pattern recognition in neuromuscular discipline. With the development of materials, sensors and computers, the research on surface electromyography at home and abroad has gradually deepened, which makes surface electromyography widely used not only in sports medicine, clinical medicine and rehabilitation medicine, but also in the fields of sports medicine, clinical medicine and rehabilitation medicine. Moreover, the pattern recognition of the ideal control signal. SEMG signal of artificial prosthesis is the basis of its application. For this reason, this paper deeply discusses how to recognize the different motion modes of lower extremity by the collected sEMG signal. Based on the non-stationary and randomness of sEMG signal, the purpose of this paper is to use modern signal processing method to find out its intrinsic characteristics, and to design pattern classifier based on modern pattern recognition theory. It can effectively identify the eigenvalues of lower extremity motion mode and provide theoretical basis for revealing the nature of sEMG signal and the practicality of multi-degree-of-freedom myoelectric control prosthesis. The main work and innovations are as follows: 1. The amplifying and filtering circuit of sEMG signal is the key to realize the EMG signal acquisition system. According to the amplitude and frequency characteristic of sEMG signal and the influence of external signal, a better filter circuit is designed in this paper. In particular, a new type of 50Hz power frequency notch circuit is designed, which can solve the adverse effects of power frequency noise on sEMG signal. 2. Using the multi-resolution analysis technology of wavelet transform, using the singular value of wavelet decomposition coefficient matrix to construct the characteristic vector of sEMG signal, combining with BP neural network and support vector machine classifier, the classification of six common movements of human lower limb is studied. The fatigue evaluation of lower extremity muscle and the recognition of road condition based on sEMG signal are completed. 3. A process neural network classification algorithm based on particle swarm optimization (PSO) is proposed, which takes into account the spatial coupling effect and time accumulation effect of collected signals, and avoids the loss of information caused by feature extraction. The automatic classification of different movement modes of human lower extremities has been completed. Compared with the traditional process neural network algorithm, the particle swarm optimization algorithm proposed in this paper greatly improves the efficiency of the algorithm by optimizing the network coefficients, and achieves the ideal classification accuracy.
【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：R318.04;TN911.7

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