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ECGdenoising
- 去除在心电信号采集过程中混入的肌电干扰 、工频干扰、基线漂移等噪声信号,避免噪声对心电信号特征点的识别和提取造成误判漏判。
gesture.rar
- 关于手势识别的一些英文文章，利用表面肌电信号,Gesture recognition on a number of English articles, the use of surface EMG
dd
- 基于模式识别的肌电信号动作分类性能研究》2010最新论文，从CNKI上买来的，现在共享给大家，希望对大家有用，-EMG pattern recognition based on the classification performance of action " 2010 latest paper, bought from the CNKI on, now for everyone to share, we hope to be useful,
RECGGdenoisiie
- 去除在心电信号采集过程中混入的肌电干扰、工频干扰、基线漂移等噪声信信号，避免噪声对心电信号特征点的识别与提取造成误判漏判 已通过测试。 -Remove the EMG interference mixed with the signal acquisition process in mind, frequency interference, baseline drift and noise channel signal to avoid noise caused by the misjudg
EMG-pattern-recognition
- 肌电信号模式识别，需要巴特沃斯滤波器，并伴有注释，适合初学者-EMG pattern recognition, we need Butterworth filter
surface-EMG-signals
- 基于非线性特征的表面肌电信号模式识别方法,用了球状李雅普洛夫指数和对支持向量机。-Surface EMG Pattern Recognition Based on the nonlinear characteristics, with a spherical Lyapunov exponent and support vector machine.
肌电信号模式识别
- 实现了两种算法，在时域内分别利用肌电信号的过零点数和波长作为特征，并利用高斯重构算法进行模式识别。(Two algorithms are implemented. In the time domain, the zero crossing point and wavelength of the EMG signal are used as the features, and the Gauss reconstruction algorithm is used for pattern recogn
sEMG feature extraction
- 提取肌电信号的时域特征，ZC，WAMP，WL，SSC，RMS。以及特征的融合。肌电信号主要是采取了脚踝关节的六个动作。在动作识别中，时域的特征最常用，而且计算复杂度低，包含的信息也充分。(The time domain features of electromyographic signals were extracted, ZC, WAMP, WL, SSC, RMS. And the fusion of features. The electromyographic signal mainl
#-nina-semimyo-master
- 基于肌电信号的手势识别，数据来自开源数据集ninapro(Hand gesture recognition based on electromyography)
LabView
- 使用delsys trigno实现肌电信号的在线采集/显示以及根据matlab的BP神经网络模型实现手势识别(Using Delsys trigno to realize the on-line acquisition / display of EMG signal and the BP neural network model of MATLAB to realize gesture recognition)