【摘要】：前臂殘疾者因為前臂和手部的缺失,身體功能和勞動能力受到極大影響。而前臂假肢通過工程方法為殘疾者提供了人工假體,部分恢復(fù)了缺少的肢體功能。近年來,由于各項科學(xué)技術(shù)的發(fā)展,越來越多的商業(yè)精密前臂假肢被投放市場,而市場上的前臂假肢也變得越來越穩(wěn)定,甚至出現(xiàn)了可以直接控制單根手指的假肢產(chǎn)品。這些假肢通過電極界面采集人體的表面肌肉電信號(surface electromyography,s EMG),處理后形成控制指令,實現(xiàn)殘疾人通過殘肢發(fā)出具體動作的肌肉電信號指令,控制高精密假肢完成具體動作。但目前市場上的精密假肢普遍價格較高,而實驗室肌電處理方法主要測試于計算機(jī)仿真平臺上,離實際使用仍有一定差距。為解決上述問題,本文嘗試設(shè)計了一套基于開源嵌入式平臺的智能假肢肌電信號采集與動作識別系統(tǒng),可實現(xiàn)對人體前臂表面肌電信號的采集、處理并產(chǎn)生控制信號用于高精度智能假肢。本設(shè)計主要包括以下方面:1.根據(jù)人體肌肉運動單元和肌電信號產(chǎn)生原理,研究了目前肌電信號采集和預(yù)處理領(lǐng)域的相關(guān)方案。設(shè)計了表面肌電信號采集系統(tǒng)、電極位置和數(shù)字信號預(yù)處理方法。2.以模式識別技術(shù)為基本原理,介紹了表面肌電信號處理中特征提取的相關(guān)特征模型和求取方法,分析對比了一種綜合時域和自回歸特征的信號特征提取方法與另一種結(jié)合時域與功率譜描述的特征提取方法。同時介紹了使用的降維算法和分類器的數(shù)學(xué)原理及實現(xiàn)方法。通過實驗分析了各方法的優(yōu)劣。3.在已有方法基礎(chǔ)上結(jié)合有限狀態(tài)機(jī)和模式識別方法,提出了一套適用于嵌入式系統(tǒng)的肌電信號處理與模式識別新方法:FSM-TSD,該方法對大量的分類問題按不同狀態(tài)進(jìn)行了拆分,降低了分類難度,提高了分類準(zhǔn)確率。4.基于商品化的肌電假肢控制相關(guān)要求,提出了一套肌電信號采集與手勢識別系統(tǒng)的設(shè)計方案,達(dá)到對采集性能與市場參數(shù)的平衡。闡述了各種算法在資源有限的嵌入式微控制器平臺上的實現(xiàn)方法。使用搭建的嵌入式平臺進(jìn)行肌電信號采集與動作識別功能的實驗。
[Abstract]:The body and labor ability of the forearm disabled is greatly affected by the loss of the forearm and the hand. The forearm prosthesis provides artificial prosthesis for the disabled by engineering, partly restoring the missing limb function. In recent years, due to the development of various science and technology, more and more commercial precision forearm prostheses have been put on the market, and the forearm prostheses on the market have become more and more stable. These prostheses collect surface electromyographys (EMG),) signals of human body through electrode interface to form control instructions, and realize that disabled people can send out specific EMG signals through residual limbs, and control the high-precision prosthesis to complete the specific actions. However, the price of precision prosthesis in the market is generally high, and the method of electromyography in laboratory is mainly tested on the computer simulation platform, which is still far from the actual use. In order to solve the above problems, this paper attempts to design a set of intelligent EMG signal acquisition and motion recognition system based on open source embedded platform, which can realize the acquisition of EMG signal on the forearm surface of human body. Process and generate control signals for high-precision intelligent prostheses. This design mainly includes the following aspects: 1. 1. According to the principle of human muscle motility unit and EMG signal generation, the related schemes in the field of EMG signal acquisition and preprocessing are studied. The surface EMG signal acquisition system, electrode position and digital signal preprocessing method. 2. 2. Based on the principle of pattern recognition, the feature models and methods of feature extraction in surface electromyography (EMG) processing are introduced. A signal feature extraction method combining time domain and autoregressive features is analyzed and compared with another feature extraction method combining time domain and power spectrum description. At the same time, the mathematical principle and realization method of dimensionality reduction algorithm and classifier are introduced. The advantages and disadvantages of each method are analyzed by experiments. On the basis of existing methods, a new EMG signal processing and pattern recognition method named: FSM-TSDs is proposed, which combines finite state machine and pattern recognition method for embedded systems. This method splits a large number of classification problems according to different states. It reduces the difficulty of classification and improves the accuracy of classification. 4. Based on the commercial requirements of myoelectric prosthesis control, a design scheme of EMG signal acquisition and gesture recognition system is proposed to achieve the balance between acquisition performance and market parameters. The implementation of various algorithms on the platform of embedded microcontroller with limited resources is described. The embedded platform is used to carry out the experiment of EMG signal acquisition and motion recognition.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R496;TN911.7

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