本文選題：肌肉 + 表面肌電信號(hào)��； 參考：《武漢理工大學(xué)》2012年碩士論文

【摘要】：當(dāng)人體運(yùn)動(dòng)時(shí),肌肉上方的皮膚表面將會(huì)產(chǎn)生出一種微弱的生物電信號(hào),即表面肌電信號(hào)(surface electromyography, sEMG),這種生物電信號(hào)可以真實(shí)地反映出人體的運(yùn)動(dòng)狀況,而且提取該信號(hào)的方法簡便,具有無痛、無創(chuàng)等優(yōu)點(diǎn)。研究人員通過運(yùn)用科學(xué)的方法,能夠從表面肌電信號(hào)中提取出反映肌肉疲勞程度、肢體運(yùn)動(dòng)意圖等有用信息,所以生物電信號(hào)在肢體康復(fù)訓(xùn)練、智能假體、醫(yī)學(xué)診斷等研究領(lǐng)域具有很高的研究價(jià)值。 由于人體是一種優(yōu)良的導(dǎo)體,其中充滿著各種電磁干擾噪聲,人體表面的肌電信號(hào)的幅值通常在mV級(jí)以下,而干擾噪聲的幅值通常在mV級(jí)以上,所以從人體皮膚表面提取的肌電信號(hào)常常淹沒在干擾噪聲之中。為了提取有效的生物信息,在進(jìn)行信號(hào)特征提取之前,需要放大有用信號(hào),濾除干擾噪聲。本文針對(duì)目前表面肌電信號(hào)研究中所存在的相關(guān)問題,提出了一種易于實(shí)現(xiàn)的下肢表面肌電信號(hào)采集與處理系統(tǒng)方案,并結(jié)合下肢康復(fù)訓(xùn)練機(jī)器人,對(duì)系統(tǒng)的實(shí)時(shí)性和穩(wěn)定性進(jìn)行實(shí)驗(yàn)測試。 本文的研究內(nèi)容主要包括以下四個(gè)方面： (1)在了解表面肌電信號(hào)國內(nèi)外研究狀況的基礎(chǔ)上,描述了肌電信號(hào)的特征、產(chǎn)生機(jī)理,表面肌電信號(hào)的特征,以及人體下肢關(guān)節(jié)運(yùn)動(dòng)與下肢肌肉之間的關(guān)系。 (2)設(shè)計(jì)了表面肌電信號(hào)采集儀的硬件電路。系統(tǒng)地闡述了電路的整體結(jié)構(gòu)設(shè)計(jì),分模塊敘述了電路的設(shè)計(jì)與組成,并對(duì)電路的原理、所采用的設(shè)計(jì)方法及所使用的元器件進(jìn)行了詳盡的介紹。 (3)設(shè)計(jì)并實(shí)現(xiàn)了表面肌電信號(hào)處理軟件系統(tǒng)。敘述了軟件的整體設(shè)計(jì)結(jié)構(gòu)、開發(fā)環(huán)境,提出并實(shí)現(xiàn)了一種簡單、高效的信號(hào)特征提取方案,利用已有的支持向量機(jī)開發(fā)包實(shí)現(xiàn)了運(yùn)動(dòng)意圖的分類識(shí)別。 (4)給出了系統(tǒng)的測試環(huán)境及實(shí)驗(yàn)測試結(jié)果。敘述了下肢康復(fù)訓(xùn)練機(jī)器人的構(gòu)造結(jié)構(gòu)、工作機(jī)制,利用TCP協(xié)議實(shí)現(xiàn)了表面肌電信號(hào)處理系統(tǒng)與機(jī)器人控制系統(tǒng)之間的無差錯(cuò)通信。 作為湖北省國際合作重點(diǎn)項(xiàng)目“多自由度下肢骨骼復(fù)位手術(shù)與康復(fù)訓(xùn)練機(jī)器人研究”的一部分,本文實(shí)現(xiàn)了對(duì)人體下肢表面肌電信號(hào)的采集、處理,并將本文的研究成果與下肢康復(fù)訓(xùn)練機(jī)器人研究成果相結(jié)合,初步實(shí)現(xiàn)了利用下肢運(yùn)動(dòng)動(dòng)作控制機(jī)器人運(yùn)動(dòng)的目標(biāo)。
[Abstract]:When the human body moves, the skin surface above the muscle will produce a weak bioelectrical signal, namely, the surface electromyography, which can truly reflect the movement of the human body.Moreover, the method of extracting the signal is simple, painless and non-invasive.By using scientific methods, researchers can extract useful information from surface EMG signals to reflect muscle fatigue, limb motion intentions, and so on, so bioelectrical signals are used in limb rehabilitation training and intelligent prosthesis.Medical diagnosis and other research areas have high research value.Because the human body is a kind of excellent conductor, which is full of various electromagnetic interference noise, the amplitude of the EMG signal on the human surface is usually below MV, and the amplitude of the interference noise is usually above the MV level.Therefore, EMG signals extracted from human skin surface are often submerged in interference noise.In order to extract effective biological information, it is necessary to amplify useful signals and filter interference noise before extracting signal features.Aiming at the problems existing in the research of surface electromyography (EMG) signals, this paper presents a scheme of collecting and processing the SEMG signals of the lower extremity which is easy to realize, and combines with the robot of lower extremity rehabilitation training.The real-time and stability of the system are tested.The research contents of this paper mainly include the following four aspects:1) on the basis of understanding the research status of surface electromyography (EMG) at home and abroad, this paper describes the characteristics of SEMG, the mechanism of EMG, the characteristics of SEMG, and the relationship between the joint motion of human lower extremity and the muscles of lower extremity.The hardware circuit of the surface EMG signal acquisition instrument is designed.The whole structure design of the circuit is described systematically. The design and composition of the circuit are described in detail. The principle of the circuit, the design method adopted and the components used are introduced in detail.The software system of surface EMG signal processing is designed and implemented.The overall design structure and development environment of the software are described. A simple and efficient signal feature extraction scheme is proposed and implemented. The existing support vector machine development kit is used to realize the classification and recognition of motion intention.The test environment and experimental results are given.The structure and working mechanism of the lower limb rehabilitation training robot are described. The error-free communication between the surface EMG signal processing system and the robot control system is realized by using the TCP protocol.As a part of Hubei Province international cooperation project, "Research on the robot of multi-degree-of-freedom lower extremity skeletal reduction and rehabilitation training", this paper has realized the acquisition and processing of EMG signals on human lower extremity surface.Combining the research results of this paper with the research results of the lower limb rehabilitation training robot, the goal of controlling the robot movement by using the lower extremity motion action is preliminarily realized.
【學(xué)位授予單位】：武漢理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：R318.0

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