本文選題：50Hz陷波器 + 小波多分辨分析　； 參考：《東北大學(xué)》2012年碩士論文

【摘要】：人體表面肌電信號(hào)是一種微弱的、復(fù)雜的生物醫(yī)學(xué)信號(hào),是由肌肉收縮而伴隨產(chǎn)生的,而肌肉的收縮是由人體神經(jīng)所控制,所以,對(duì)表面肌電信號(hào)的分析可以反映出神經(jīng)控制肌肉運(yùn)動(dòng)變化趨勢(shì),進(jìn)而在臨床診斷、康復(fù)醫(yī)療等領(lǐng)域進(jìn)行研究。本文基于“985工程”項(xiàng)目“康復(fù)機(jī)器人關(guān)鍵技術(shù)的研究與應(yīng)用”,針對(duì)表面肌電信號(hào)的采集系統(tǒng)的搭建、表面肌電信號(hào)的預(yù)處理以及表面肌電信號(hào)的特征提取和分類問題,做了深入的研究,成功的搭建了表面肌電信號(hào)的采集系統(tǒng)；解決了表面肌電信號(hào)噪聲去噪問題；成功的設(shè)計(jì)了表面肌電信號(hào)模式分類器,并得到了較高的動(dòng)作識(shí)別結(jié)果。 本文設(shè)計(jì)應(yīng)用的表面肌電信號(hào)采集和處理系統(tǒng)是一個(gè)十通道多參數(shù)的生理信號(hào)采集設(shè)備(FlexComp Infiniti),配合Biograph Infiniti軟件對(duì)采集到的表面肌電信號(hào)進(jìn)行處理。通過多次的信號(hào)采集實(shí)驗(yàn),證明此系統(tǒng)能夠很好地采集和處理表面肌電信號(hào)。 在表面肌電信號(hào)的采集過程中,常常伴隨著諸多的干擾信號(hào),其中除了主要的50Hz工頻干擾外,也夾雜著其它噪聲信號(hào),需要對(duì)信號(hào)進(jìn)行去噪處理。 Biograph Infiniti軟件自帶與MATLAB軟件的接口程序。本文應(yīng)用MATLAB仿真軟件設(shè)計(jì)了IIR50Hz陷波器和FIR50Hz陷波器,在盡量不影響有用信號(hào)中50Hz頻率信號(hào)的情況下,對(duì)信號(hào)中夾雜的50Hz干擾進(jìn)行去除。經(jīng)過實(shí)驗(yàn)驗(yàn)證,證明FIR50Hz濾波器更能有效的去除表面肌電信號(hào)中的50Hz工頻干擾。 本文在去除表面肌電信號(hào)的其它噪聲方面,首先運(yùn)用小波多分辨分析對(duì)信號(hào)進(jìn)行閾值去噪。并將小波去噪和數(shù)字濾波器的基本理論相結(jié)合,構(gòu)造出了基于小波變換的數(shù)字濾波器。經(jīng)過實(shí)驗(yàn)驗(yàn)證,證明基于小波變換的數(shù)字濾波器能夠更好的去除表面肌電信號(hào)的其它噪聲。 此外,本文在對(duì)表面肌電信號(hào)去噪研究后,還對(duì)經(jīng)過去噪的信號(hào)進(jìn)行了處理。通過小波變換對(duì)信號(hào)進(jìn)行特征提取,并且應(yīng)用BP神經(jīng)網(wǎng)絡(luò)對(duì)上肢四種運(yùn)動(dòng)進(jìn)行模式分類,得到了識(shí)別率較高的實(shí)驗(yàn)結(jié)果。 通過本文的研究,提出了一套可以對(duì)肌電信號(hào)進(jìn)行采集和分析的算法和方案。在未來的工作中,將繼續(xù)對(duì)各種特征處理的方法進(jìn)行研究,找出更適合肌電信號(hào)的方法,同時(shí)也嘗試不同分類器對(duì)分類性能的影響,提高系統(tǒng)性能,并將其應(yīng)用于康復(fù)機(jī)器人中。
[Abstract]:The surface EMG signal is a weak, complex biomedical signal that is accompanied by muscle contraction, which is controlled by the human body's nerves, so, The analysis of surface EMG signals can reflect the trend of neuro-controlled muscle movement and then be studied in the field of clinical diagnosis and rehabilitation. Based on "Project 985", "Research and application of key technology of rehabilitation robot", this paper aims at the construction of surface electromyography acquisition system, pretreatment of surface electromyography signal and feature extraction and classification of surface electromyography signal. In this paper, the acquisition system of surface EMG signal is set up successfully; the problem of noise denoising is solved; the mode classifier of SEMG signal is designed successfully, and the result of motion recognition is obtained. The surface EMG signal acquisition and processing system designed and applied in this paper is a 10-channel multi-parameter physiological signal acquisition equipment, FlexComp Infinitig, which is used to process the collected surface EMG signal with Biograph Infiniti software. Through many signal acquisition experiments, it is proved that the system can collect and process surface EMG signal well. In the process of surface EMG signal acquisition, many interference signals are often accompanied, except for the main 50Hz power frequency interference, there are also other noise signals, which need to be de-noised. Biograph Infiniti software comes with MATLAB software interface program. In this paper, the IIR50Hz notch and FIR50Hz notch are designed by using MATLAB simulation software. The 50Hz interference in the signal is removed without affecting the 50Hz frequency signal in the useful signal as much as possible. The experimental results show that the FIR50Hz filter is more effective to remove the 50Hz power frequency interference from the surface EMG signal. In this paper, in order to remove other noises of surface EMG signal, wavelet Multiresolution analysis is first used to de-noise the signal. A digital filter based on wavelet transform is constructed by combining wavelet denoising with the basic theory of digital filter. The experimental results show that the digital filter based on wavelet transform can better remove other noises of surface EMG signal. In addition, after the research of surface EMG signal denoising, the de-noised signal is processed. The wavelet transform is used to extract the feature of the signal and the BP neural network is used to classify the four movements of the upper limb. The experimental results with high recognition rate are obtained. Through the research in this paper, a set of algorithms and schemes can be used to collect and analyze EMG signals. In the future work, we will continue to study various feature processing methods to find out more suitable methods for EMG signals. At the same time, we will also try to improve the performance of the system by the influence of different classifiers on classification performance. It is applied to rehabilitation robot.
【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：R318.04;TN911.7

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