【作者】 杨沛沛；

【导师】 王永骥；

【作者基本信息】 华中科技大学 ， 控制理论与控制工程， 2009， 硕士

【摘要】 随着康复医学的发展,康复机器人的研制越来越受到人们的重视,肌电信号模式识别系统是康复机器人的重要组成部分,通过肌电信号对病人的运动意识进行快速、准确地识别,是康复机器人完成康复训练的必要前提。本文通过对特征提取方法与模式识别方法的研究,结合肌电信号自身的特点,提出用于肌电信号模式识别的具体算法,并且使用真实的肌电数据对算法进行仿真和验证。根据康复机器人的需求,具体实施了该算法,设计并开发了一套肌电信号模式识别系统,通过对前臂肌电信号的检测与处理,实时地对腕部动作进行识别,并通过实验对算法进行验证。本文首先介绍当前国内外对肌电信号模式识别研究的现状,研究应用于肌电的各种特征提取和处理方法以及模式识别方法,比较各自的优缺点。然后介绍肌电采集系统的结构和各部分需要考虑的问题,叙述采集系统的实现过程,该部分是进行肌电信号模式识别的前提。然后文章详细叙述模式识别系统的实现,根据肌电信号的特性和各种算法的特点选择小波包变换作为特征提取的方法,针对小波包变换缺乏时不变性的缺点,用结点能量代替原始系数构成特征向量,从而克服该缺点;确定特征空间降维方法,针对其实施过程中遇到的问题提出解决方案,并选择人工神经网络作为模式分类器。再具体描述上述算法的实现方法和过程,详述了识别系统的结构和基本工作原理。最后叙述了实验过程和结果,并对其结果进行简要分析与总结。在本实验中,对测试样本的识别正确率达到了94.17%.本文通过本系统的研究与开发,提出了一套可以对肌电信号进行实时识别的算法和实现方案,并且针对实现过程中遇到的问题提出解决方案。在未来的工作中,将继续对各种特征处理的方法进行研究,找出更适合肌电信号的方法,同时也尝试不同分类器对分类性能的影响,提高系统性能,并将其应用于康复机器人中。更多还原

【Abstract】 By the development of convalescence medicine, the research of rehabilitation robot is attracting more and more attention of people, and electromyographic (EMG) plays an important role in it by recognizing the sports consciousness of patients, which is an indispensable procedure of rehabilitation training. According to the specific characteristic of EMG, this paper proposes an algorithm used to recognize the pattern of EMG by studying a series of algorithms of feature extraction and pattern recognition, and tested this algorithm using actual EMG signal. This algorithm is also implemented by designing and developing an EMG pattern recognition system according to the requirement of rehabilitation robot, which is also used to certificate the effect of the proposed algorithm.Firstly, this paper introduces the current condition of the research of EMG pattern recognition, studies and compares a series of approaches of feature extraction and pattern recognition. Then the structure of the EMG collecting system which is necessary for pattern recognition is introduced with some problems to consider. After that, this paper describes the procedure of the implementation of the EMG pattern recognition system in detail. According to the characteristic of EMG, wavelet package analysis is used to extract the feature, and the node energy is used to construct the feature vector instead of the original coefficients of wavelet package decomposition to resolve the time-invariance problem encountered. Then the algorithm of feature reduction is determined and a method to solve the problem encountered when calculating the reduction matrix. Then the implementation of the algorithm and the construct of the system were described. At last, the process and result of the experiment were described and a brief analysis was presented. In this experiment, the accuracy of recognition reaches up to 94.17%.By the development of this system, we proposed a series of algorithms and implementing approaches of EMG pattern recognition. In the future, we will continue to study the various algorithms of pattern recognition and find which one is more suitable for the EMG. We will also try different kinds of classifiers and compare the performance of them and apply our system to the rehabilitation robot.更多还原