發(fā)布時(shí)間：2018-06-15 00:25

  本文選題：表面肌電信號(hào) + 模式識(shí)別�。� 參考：《上海師范大學(xué)》2017年碩士論文

【摘要】：表面肌電信號(hào)(surface electromyography,sEMG)是人體肌肉收縮時(shí)產(chǎn)生的生物電信號(hào)。隨著國(guó)內(nèi)外學(xué)者的不懈努力,sEMG已經(jīng)被廣泛應(yīng)用于臨床檢測(cè)、康復(fù)工程以及假肢手控制等領(lǐng)域中。目前,基于sEMG的假肢手控制技術(shù)已然成為研究的熱點(diǎn)。與傳統(tǒng)傳感器相比,MYO臂環(huán)具有不受場(chǎng)地限制、交互自然、穿戴方便以及性?xún)r(jià)比高等優(yōu)點(diǎn),非常適合用來(lái)控制假肢手。所以,本文的目的在于研究一種基于MYO臂環(huán)的肌電假肢手控制技術(shù),通過(guò)算法實(shí)現(xiàn)對(duì)人手動(dòng)作模式識(shí)別和人手抓取力的預(yù)測(cè),并結(jié)合在PC端開(kāi)發(fā)的假肢手肌電控制系統(tǒng)進(jìn)行驗(yàn)證。本文主要研究工作如下:(1)人手動(dòng)作模式識(shí)別研究。本實(shí)驗(yàn)采用六階巴特沃斯帶通濾波器對(duì)MYO臂環(huán)采集的sEMG進(jìn)行預(yù)處理,并提取5種時(shí)域特征,采用PCA和BP神經(jīng)網(wǎng)絡(luò)相結(jié)合的方法對(duì)人手動(dòng)作模式進(jìn)行分類(lèi)。實(shí)驗(yàn)結(jié)果表明,運(yùn)用PCA將特征樣本映射到20維時(shí),人手動(dòng)作模式的識(shí)別率可達(dá)99%。(2)人手抓取力預(yù)測(cè)技術(shù)研究。本實(shí)驗(yàn)選取絕對(duì)平均值(MAV)和均方根(RMS)作為特征,以抓取力的八個(gè)檔次為輸出,建立了基于BP神經(jīng)網(wǎng)絡(luò)的抓取力預(yù)測(cè)模型。實(shí)驗(yàn)結(jié)果表明,確定抓取力按照大小分檔的平均識(shí)別率達(dá)到了93.83%,能夠滿(mǎn)足假肢手控制的基本要求。(3)假肢手肌電控制系統(tǒng)設(shè)計(jì)。設(shè)計(jì)了一套基于MFC的肌電控制系統(tǒng),該系統(tǒng)能夠采集并分析sEMG,進(jìn)而獲取人手動(dòng)作模式的活動(dòng)意圖,且對(duì)手指抓取力進(jìn)行實(shí)時(shí)預(yù)測(cè),經(jīng)串口對(duì)假肢手進(jìn)行驅(qū)動(dòng)控制。最終,應(yīng)用該系統(tǒng)驗(yàn)證了本課題方案的可行性。該肌電控制系統(tǒng)根據(jù)sEMG實(shí)現(xiàn)人手動(dòng)作模式的實(shí)時(shí)分類(lèi)和抓取力的實(shí)時(shí)預(yù)測(cè)。所提取的手部動(dòng)作意圖和抓取力可轉(zhuǎn)換成不同的控制命令,能夠提供一種有效的基于生物電信號(hào)的人機(jī)交互模式。該系統(tǒng)主要?jiǎng)?chuàng)新性的工作在于將高性?xún)r(jià)比MYO臂環(huán)應(yīng)用到假肢手的控制中,實(shí)現(xiàn)了人手動(dòng)作模式和抓取力的在線(xiàn)控制,在線(xiàn)平均識(shí)別率可達(dá)92%。而且,該系統(tǒng)安裝和使用方便、抗干擾能力強(qiáng)以及具有很高的可控性,可以很好地滿(mǎn)足殘疾人對(duì)假肢手控制的需求。
[Abstract]:Surface electromyography (EMG) is a bioelectric signal produced when human muscles contract. With the unremitting efforts of scholars at home and abroad, SEMG has been widely used in clinical detection, rehabilitation engineering and prosthetic hand control. At present, the control technology of prosthetic hand based on SEMG has become a hot spot. Compared with traditional sensors, MYO arm ring has the advantages of no limitation of site, natural interaction, easy to wear and high cost performance, so it is very suitable for the control of prosthetic hand. Therefore, the purpose of this paper is to study a myoelectric prosthetic hand control technology based on MYO arm ring. The EMG control system of prosthetic hand was developed on PC. The main work of this paper is as follows: 1. In this experiment, the sixth order Butterworth band-pass filter is used to preprocess the SEMG collected by MYO arm ring, and five time domain features are extracted, and the manual action pattern is classified by PCA and BP neural network. The experimental results show that when PCA is used to map feature samples to 20 dimensions, the recognition rate of manual action pattern can reach 99%. In this experiment, the absolute mean value (MAV) and RMS (mean square root) are selected as the characteristics, and the prediction model of grab force based on BP neural network is established with eight grades of grab force as the output. The experimental results show that the average recognition rate of grasping force is 93.833.It can meet the basic requirements of prosthetic hand control. A set of electromyoelectric control system based on MFC is designed. The system can collect and analyze sEMG, and then obtain the active intention of the manual movement mode, and predict the finger grasping force in real time, and carry on the drive control to the prosthetic hand through the serial port. Finally, the feasibility of the project is verified by using the system. According to SEMG, the EMG realizes the real-time classification of manual action mode and the real-time prediction of grasping force. The extracted hand motion intention and grip force can be converted into different control commands, which can provide an effective human-computer interaction mode based on bioelectric signals. The main innovative work of this system is to apply the MYO arm ring with high performance to price ratio in the control of prosthetic hand. The on-line control of manual movement mode and grip force is realized, and the on-line average recognition rate can reach 92%. Moreover, the system is easy to install and use, has strong anti-interference ability and has a high controllability, which can meet the needs of the disabled in the control of prosthetic hands.
【學(xué)位授予單位】：上海師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：R496;TP273

【參考文獻(xiàn)】

相關(guān)期刊論文 前8條

1 卜峰;李傳江;陳佳佳;李歡;郭偉海;;基于ARM的肌電假肢手控制器[J];上海大學(xué)學(xué)報(bào)(自然科學(xué)版);2014年04期

2 丁其川;趙新剛;韓建達(dá);;基于肌電信號(hào)的上肢多關(guān)節(jié)連續(xù)運(yùn)動(dòng)估計(jì)[J];機(jī)器人;2014年04期

3 黃鵬程;楊慶華;鮑官軍;張立彬;;基于幅值立方和BP神經(jīng)網(wǎng)絡(luò)的表面肌電信號(hào)特征提取算法[J];中國(guó)機(jī)械工程;2012年11期

4 楊大鵬;趙京東;姜力;劉宏;;多抓取模式下人手握力的肌電回歸方法[J];哈爾濱工業(yè)大學(xué)學(xué)報(bào);2012年01期

5 王新慶;劉伊威;楊大鵬;樊紹巍;劉宏;;基于EMG的假手實(shí)時(shí)力跟蹤控制[J];沈陽(yáng)工業(yè)大學(xué)學(xué)報(bào);2012年01期

6 谷秀川;呂廣明;;基于小波變換的肌電信號(hào)的多尺度分解[J];數(shù)學(xué)的實(shí)踐與認(rèn)識(shí);2010年24期

7 李媛媛;陳香;張旭;楊基海;;基于ART2神經(jīng)網(wǎng)絡(luò)的手勢(shì)動(dòng)作肌電信號(hào)識(shí)別[J];中國(guó)科學(xué)技術(shù)大學(xué)學(xué)報(bào);2010年08期

8 尹少華;楊基海;梁政;陳香;任焱暄;;基于遞歸量化分析的表面肌電特征提取和分類(lèi)[J];中國(guó)科學(xué)技術(shù)大學(xué)學(xué)報(bào);2006年05期

相關(guān)博士學(xué)位論文 前3條

1 王新慶;基于肌電信號(hào)的仿人型假手及其抓取力控制的研究[D];哈爾濱工業(yè)大學(xué);2012年

2 宋全軍;人機(jī)接觸交互中人體肘關(guān)節(jié)運(yùn)動(dòng)意圖與力矩估計(jì)[D];中國(guó)科學(xué)技術(shù)大學(xué);2007年

3 劉建成;模糊模型的智能學(xué)習(xí)方法與應(yīng)用研究[D];中南大學(xué);2005年

相關(guān)碩士學(xué)位論文 前2條

1 陳棟金;機(jī)器人多指手的力規(guī)劃及其同步協(xié)調(diào)控制[D];哈爾濱工業(yè)大學(xué);2012年

2 時(shí)改杰;動(dòng)作表面肌電信號(hào)的特征提取方法研究[D];上海交通大學(xué);2008年

，

本文編號(hào)：2019679