本文選題：表面肌電信號(hào) + 非線性分析�。� 參考：《上海交通大學(xué)》2012年碩士論文

【摘要】：人體動(dòng)作電信號(hào)由神經(jīng)和肌肉組成的運(yùn)動(dòng)單元產(chǎn)生,然后控制肌肉進(jìn)行協(xié)同作用,從而完成人體動(dòng)作。不同的電信號(hào)驅(qū)動(dòng)不同的動(dòng)作。在動(dòng)作完成的整個(gè)過(guò)程中,這些電信號(hào)通過(guò)人體組織在皮膚表面上輸出,被皮膚處的電極等設(shè)備采集到。采集到的電信號(hào)稱為表面肌電信號(hào)。表面肌電信號(hào)與肌肉活動(dòng)情況和功能特性之間存在著不同程度的關(guān)聯(lián)性,在一定程度上反映了神經(jīng)肌肉的狀況和活動(dòng)情況。因此,表面肌電信號(hào)在臨床醫(yī)學(xué)、運(yùn)動(dòng)醫(yī)學(xué)、人機(jī)工效學(xué)、康復(fù)醫(yī)學(xué)、神經(jīng)生理學(xué)、電生理學(xué)等領(lǐng)域被廣泛應(yīng)用。 目前,關(guān)于動(dòng)作表面肌電信號(hào)的非線性特性研究還處于初級(jí)探索階段。在已有的表面肌電信號(hào)采集方法和技術(shù)的基礎(chǔ)上,本文了設(shè)計(jì)了表面肌電信號(hào)的采集實(shí)驗(yàn),采集到人體前臂內(nèi)翻、外翻、握拳、展拳、上切和下切六類動(dòng)作表面肌電信號(hào)作為研究對(duì)象。利用非線性時(shí)間序列分析方法對(duì)表面肌電信號(hào)的非線性特性進(jìn)行研究,驗(yàn)證了表面肌電信號(hào)的非線性特性,表明動(dòng)作表面肌電信號(hào)是一混沌信號(hào)。這對(duì)于深入認(rèn)識(shí)神經(jīng)肌肉系統(tǒng)的功能活動(dòng)規(guī)律及其實(shí)質(zhì)、建立更加科學(xué)合理的肌肉功能非損傷性評(píng)價(jià)技術(shù)均具有重要的價(jià)值。 為了進(jìn)一步了解動(dòng)作表面肌電信號(hào)的非線性特性,本文主要利用小波變換和希爾伯特-黃變換對(duì)動(dòng)作表面肌電信號(hào)進(jìn)行了多尺度分解,進(jìn)而對(duì)每一個(gè)尺度上的動(dòng)作表面肌電信號(hào)的非線性特性進(jìn)行了研究,把動(dòng)作表面肌電信號(hào)的非線性特性在不同的尺度上進(jìn)行展開,更加了解了動(dòng)作表面肌電信號(hào)的非線性特性。 最后,為了提高動(dòng)作表面肌電信號(hào)的識(shí)別率,本文提出一種將非線性分析和多尺度分析結(jié)合的方法。該方法從其非線性和非平穩(wěn)特性的角度出發(fā),引入了多尺度非線性特征,并應(yīng)用到人體前臂六類動(dòng)作表面肌電信號(hào)的模式識(shí)別中。將多尺度非線性特征輸入支持向量機(jī),并結(jié)合核主元分析方法,使動(dòng)作表面肌電信號(hào)的平均識(shí)別率達(dá)到98%。結(jié)果表明,利用多尺度非線性特征對(duì)動(dòng)作表面肌電信號(hào)進(jìn)行模式識(shí)別效果良好。
[Abstract]:Human action signals are generated by motor units composed of nerves and muscles, and then control the muscles for synergistic action, thus accomplishing human actions. Different electrical signals drive different movements. In the whole process of action, these electrical signals are output from human tissues on the skin surface and collected by devices such as electrodes in the skin. The collected electrical signals are called surface EMG signals. There is some correlation between surface EMG signal and muscle activity and functional characteristics, which reflects the condition and activity of neuromuscular to some extent. Therefore, surface electromyography is widely used in clinical medicine, sports medicine, ergonomics, rehabilitation medicine, neurophysiology, electrophysiology and so on. At present, the study of nonlinear characteristics of action surface EMG signal is still in the primary stage. On the basis of existing methods and techniques of collecting surface EMG signals, this paper designs an experiment to collect surface EMG signals, which can collect human forearm varus, valgus, clenched fist, extended fist, and so on. Six types of action surface electromyography (EMG) were studied. The nonlinear characteristics of surface EMG signal are studied by using nonlinear time series analysis method, and the nonlinear characteristics of SEMG signal are verified, which indicates that the action surface EMG signal is a chaotic signal. This is of great value for further understanding the functional activity and essence of neuromuscular system and establishing a more scientific and reasonable non-injurious evaluation technique for muscle function. In order to further understand the nonlinear characteristics of action surface EMG signal, wavelet transform and Hilbert-Huang transform are mainly used to decompose the action surface EMG signal. Furthermore, the nonlinear characteristics of EMG signals on each scale are studied, and the nonlinear characteristics of EMG signals are expanded on different scales to understand the nonlinear characteristics of EMG signals. Finally, in order to improve the recognition rate of EMG signals, a method combining nonlinear analysis and multi-scale analysis is proposed. From the point of view of its nonlinear and non-stationary characteristics, this method introduces multi-scale nonlinear features, and is applied to the pattern recognition of six kinds of EMG signals on the forearm. The multi-scale nonlinear feature is input into the support vector machine and the kernel principal component analysis method is used to make the average recognition rate of the EMG signal on the action surface up to 98. The results show that the pattern recognition of EMG signals on the action surface is effective by using multi-scale nonlinear features.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TH772

【共引文獻(xiàn)】

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1 王從政;基于DSP的手勢(shì)交互系統(tǒng)實(shí)現(xiàn)[D];中國(guó)科學(xué)技術(shù)大學(xué);2011年

2 李媛媛;基于ART2神經(jīng)網(wǎng)絡(luò)的手勢(shì)動(dòng)作SEMG信號(hào)模式識(shí)別研究[D];中國(guó)科學(xué)技術(shù)大學(xué);2009年

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本文編號(hào)：1868320