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《计算机测量与控制》2014,(4)
针对时域特征参数在表面肌电信号(SEMG)模式识别过程中的局限性,提出一种小波包变换(WPT)和线性判别分析(LDA)相结合的新方法;通过虚拟仪器采集桡侧腕屈肌和肱桡肌两路表面肌电信号,应用小波包变换对表面肌电信号进行多尺度分解,提取小波包系数并计算其均方根作为特征参数,应用线性判别分析对表面肌电信号数据进行分类识别;实验结果表明,采用此方法成功地从表面肌电信号中识别握拳、展拳、手腕内翻和手腕外翻4种动作,与时域参数相比,此方法更能有效提取表面肌电信号信息,且有较高的动作识别率,识别率高达98.2%。 相似文献
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提出了一种带触觉反馈的肌电假手模糊控制方法.该方法在有效识别肌电信号(MES)的基础上,实现了基于触觉与肌电信号的假手仿生控制.为了消除肌电信号的个体差异,使其能可靠应用于非特定人场合,应用互功率谱比值法对肌电信号进行处理,并分析互功率谱的各项参数和对应肢体动作变化之间的关系,消除了肌电信号实际测量中不可测噪声的干扰.采用特别设计的模糊控制器控制假手动作的步进量,实现了肌电假手对多种不同物体的稳定抓取.实验表明这种组合方法不仅可以获得较高的动作模式识别率,而且能有效地降低被抓取物体的损坏. 相似文献
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文章选取时域分析法对表面肌电信号的提取其特征值,意在于能够得到能较好地表征肌电信号的特征向量,使得之后的分类器能够有效地对表面肌电信号进行分类识别。在对信号进行识别分类识别时,所设计的小波神经网络可以将各动作信号特征值转化为线性组合,简化动作的分类识别过程。 相似文献
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肌电信号的功率谱分析方法 总被引:8,自引:0,他引:8
当用传统的检测方法对肌电信号(EMG)进行处理时,会出现较大的个体差异,针对这一不足,我们对其在频域中作具体的分析,提出了一种新的表面肌电信号特征提取方法--功率谱比值法.并在非特定人的条件下,经过多次实验确定实际所用肌电信号的主要频率范围和功率谱的各项参数,将功率谱比值法应用于前臂肌群的表面EMG特征提取,减小了个体差异,进而实现非特定人的动作模式识别. 相似文献
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为了有效提取表面肌电信号SEMG(Surface Electromyographic)的特征,更好的识别人体上肢运动模式,提出了一种小波包核主元分析(WPKPCA)和支持向量机(SVM)相结合的新方法。通过虚拟仪器采集桡侧腕屈肌和肱桡肌两路表面肌电信号,应用小波包核主元分析法对表面肌电信号进行特征提取,采用支持向量机对表面肌电信号特征数据进行分类识别。实验结果表明,采用此方法能够从表面肌电信号中识别出握拳、展拳、手腕内翻和手腕外翻4种动作,更能有效提取表面肌电信号信息,动作识别率高达98%。 相似文献
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复杂性测度在肌电信号模式识别中的应用 总被引:1,自引:0,他引:1
针对表面肌电信号(SEMG)的非平稳特性,提出了一种以复杂性测度和支持向量机(SVM)相结合的肌电信号模式识别新方法。肌电信号的复杂度作为一种新的肌电信号特征,算法简单。支持向量机是一种新的机器学习机制。通过对采集的四通道表面肌电信号进行分析,提取其复杂性测度信息构建特征矢量,利用“一对一”分类策略和二叉树构建的多类支持向量机分类器,很好地实现了对前臂的八种动作表面肌电信号的模式分类。实验表明,由支持向量机对肌电信号的复杂度特征进行分类,具有很好的稳定性和准确率,为肌电信号及其它非平稳生理电信号的模式分类提供了一种新思路。 相似文献
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针对表面肌电信号的特点,提出了一种应用非线性主分量分析(PCA)提取表面肌电信号特征的新方法.该方法在表面肌电信号滤波的基础上,采用非线性PCA方法完成数据压缩,将多路表面肌电信号转换为一维的特征数据主元,并以主元曲线的形式输出特征提取结果.本文采用基于自组织神经网络的非线性PCA对手臂尺侧腕伸肌和尺侧腕屈肌的两路表面肌电信号进行主元提取,试验结果表明,四种手部运动模式(握拳、展拳、腕外旋、腕内旋)对应的表面肌电信号利用该方法处理后,得到的主元曲线具有很好的类区分性,依据所得主元曲线的形状特征可以有效地进行手部动作类别的识别. 相似文献
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M. Hariharan C.Y. Fook R. Sindhu Bukhari Ilias Sazali Yaacob 《Computers & Electrical Engineering》2012
The selection of most suitable mother wavelet function is still an open research problem in various signal and image processing applications. This paper presents a comparative study of different wavelet families (Daubechies, Symlets, Coiflets, and Biorthogonal) for analysis of wrist motions from electromyography (EMG) signals. EMG signals are decomposed into three levels using discrete wavelet packet transform. From the decomposed EMG signals, root mean square (RMS) value, autoregressive (AR) model coefficients (4th order) and waveform length (WL) are extracted. Two data projection methods such as principal component analysis (PCA) and linear disciminant analysis (LDA) are used to reduce the dimensionality of the extracted features. Probabilistic neural network (PNN) and general regression neural network (GRNN) are employed to classify the different types of wrist motions, which gives a promising accuracy of above 99%. From the analysis, we inferred that ‘Biorthogonal’ and ‘Coiflets’ wavelet families are more suitable for accurate classification of EMG signals of different wrist motions. 相似文献
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Osamu Fukuda Jonghwan Kim Isao Nakai Yasunori Ichikawa 《Artificial Life and Robotics》2011,16(1):90-93
This article presents the control method for a 5-fingered artificial hand using electromyography (EMG) signals. Our targeted
artificial hand is driven by pneumatic actuators to reduce its weight, and we use ON/OFF solenoid valves instead of electro
pneumatic regulators to simplify the control system. The pneumatic hand has 15 degrees of freedom, and it seems difficult
to reproduce all the finger motions from the EMG signals only. Therefore, we describe typical hand motions using a Petri net,
and control the finger motions efficiently based on this model. Each state of the Petri net indicates one step in the hand
posture to complete the intended motion. Simultaneously, this state corresponds to the ON/OFF pattern of the 15 solenoid valves.
This enables the operator to control the 5-fingered dexterous hand smoothly, transiting the state in the Petri net according
to the EMG motion signals. We conducted an experiment to verify the validity of the proposed method. In the experiment, five
typical motions (spherical grasp, power grip, hook grip, key grip, precision grip) were successfully performed using the 6-channel
EMG signals measured from the operator’s forearm. 相似文献
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肌电信号的拾取和预处理 总被引:10,自引:0,他引:10
表面EMG属于生物电的范畴,其特点是信号微弱,易受干扰.针对表面EMG的特点专门设计了表面电极和信号调理电路,对信号处理电路的特性分析表明,所设计的处理电路可以有效提取10~500 Hz的肌电信号,并能对50 Hz的工频干扰起到很好的抑制作用,该电路在实际应用时取得了理想的实验结果. 相似文献
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应用小波变换和ICA方法的肌电信号分解 总被引:2,自引:0,他引:2
基于单通道、短时真实肌电(EMG)记录和模拟EMG信号,提出一种改进的肌电信号分解方法。首先应用小波滤波、硬阈值估计等方法去除背景噪声和白噪声,并将独立成分分析(ICA)方法和小波滤波方法相结合去除工频干扰信号,然后再进行幅度滤波,从而提高了系统的速度和强健性。在运动单元动作电位(MUAP)聚类以及从原始信号中去除已识别的MUAP波形等方面也进行了改进。与已有的EMG分解方法相比,本文方法更快速、稳定。 相似文献
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仿人足底肌电特征的机器人行走规划 总被引:1,自引:0,他引:1
模仿人类行走规律是规划双足机器人运动的基础.以往模仿人类步态主要通过视觉方法或惯性模块测量(Inertia measurement unit, IMU)方法捕捉人体特征点轨迹.这些方法不考虑零力矩点(Zero moment point, ZMP)的相似性.为解决该问题,本文提出了一种基于足底肌电信号(Electromyography, EMG)和惯性模块测量信号的混合运动规划方法.该方法通过测量足底肌电信号计算出足底压力中心的位置以及踝关节扭矩,结合惯性模块所测量的人体躯干和双足轨迹,来规划双足机器人的步态.首先,用肌电仪测量足底肌电信号,用惯性测量模块测量人体各肢体部分的姿态轨迹,经数据标定后作为仿人机器人的运动参考; 然后,通过预观控制输出稳定的步态.为确保仿人行走的效果,基于人体相似性对运动数据进行了步态优化.实验验证和分析表明, EMG信号超前ZMP约160ms,利用这个特性实现了对压力点位置的有效预测,提高了机器人在线模仿人类行走的稳定性. 相似文献
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提出了基于肌电信号(EMG)的无声语音识别系统。由于该系统是通过EMG信号而非声音信号进行识别,因此可应用于高噪声环境和帮助失去发音能力的人实现无声交流,有着良好的应用前景。关于该系统的实现,提出了以下方法:实验时使用0—9十个中文数字,由受试者不发声地重复说出,从三块面部肌肉采集EMG信号;对EMG信号进行小波变换,获取变换系数矩阵后提取其能量值,构造特征矢量送入BP神经网络分类器分类。实验表明,基于小波变换的特征提取方法是一种有效的方法.适用于类似EMC信号的非平稳生理信号。 相似文献
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传统基于肌电(Electromyography,EMG)的运动识别方法多是利用训练后的固定参数模型,分类已预先定义的有限个目标动作,但对肌肉疲劳导致的肌电变化,以及未定义的外部动作等干扰因素无能为力.针对这一问题,提出一种自更新混合分类模型(Self-update hybrid classification model,SUHC),该模型融合了用于排除外部动作干扰的一类支持向量机(Support vector machine,SVM),以及用于分类目标动作数据的多类线性判别分析(Linear discriminant analysis,LDA),并引入自更新机制以对抗肌电时变性干扰.通过手部动作识别实验验证提出方法的效果,在肌电大幅变化干扰下,SUHC的目标动作识别精度达到89%,对比传统的支持向量机、多层感知器(Multiple layer perceptron,MLP)和核线性判别分析(Kernel LDA,KLDA),提高了约18%,并且SUHC具备排除外部动作干扰能力,排除精度高达93%. 相似文献
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针对基于肌电(EMG)信号的情感识别问题,提出了一种新颖的特征选择方法.该方法用相关性分析对原始特征降维,再利用改进的禁忌搜索算法结合Fisher分类器,对高兴、厌恶、悲伤、惊奇、愤怒和恐惧6种肌电信号的情感进行特征选择.实验结果表明,该方法能以较少的特征获得较高的识别率,得到了有利于情感识别系统建立的有效特征子集. 相似文献
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Experimental Study of an EMG-Controlled 5-DOF Anthropomorphic Prosthetic Hand for Motion Restoration
Dapeng Yang Li Jiang Qi Huang Rongqiang Liu Hong Liu 《Journal of Intelligent and Robotic Systems》2014,76(3-4):427-441
In this paper, we attempted to evaluate the performance of an electromyography (EMG)-controlled 5-DOF prosthetic hand on ten transradial amputees. The proposed prosthesis is composed of a five-fingered hand, a passive wrist, and a customized socket for each subject. The EMG control methods included both a commonly used pattern recognition-based scheme (DD-SVM) and a novel digital encoding strategy (double-channel template matching (DCTM)). A virtual 3D hand platform was developed for training the subjects and rapidly testing the control methods. For each subject, the performance of the EMG control methods was firstly measured by off-line classification accuracy; then, according to the accuracy, a particular control method was selected and embedded in the EMG controller for further validation on ordinary daily life activities. Our experiments were conducted to test not only the hand’s grasp ability but also other multifinger cooperation skills. The result indicated that the subjects of rich control experience can accomplish several intuitive motion control over their hands. However, the kinds of the motions and their relative recognition accuracy may depend on some individual differences, such as the amputation level, the activity of the residual nerve-muscle system, and the richness of control experience. Meanwhile, the proposed digital encoding method, DCTM, which only utilized two channels of EMG, was necessary for those amputees with few available control signals. This paper suggested that the EMG control method should be differently considered according to the particular condition of each subject. 相似文献