Classification of Hand Manipulation Using BP Neural Network and Support Vector Machine Based on Surface Electromyography Signal <ce:cross-ref id='cecref0011' refid='fn1'> <ce:sup loc='post'>*</ce:sup> </ce:cross-ref>

Yang Lu; Gang Lu; Xianye Bu; Yajing Yu

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Classification of Hand Manipulation Using BP Neural Network and Support Vector Machine Based on Surface Electromyography Signal *

机译：基于表面肌电信号的使用BP神经网络和支持向量机的手部分类 *

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In this paper, we focus on the method of classifying the surface electromyography (sEMG) signals based on hand manipulations via time series of the measured data. In order to represent dynamical characteristics of sEMG, a stochastic dynamic process is included in it based on the maximum likelihood estimation (MLE) principle. By using the EM algorithm, the RMS, WAMP, AR, Wavelet, GMM and HMM feature of the signal can be identified easily. Ten people of different time series data sets of different hand grasps and in-hand manipulations captured from different subjects are collected. The BP and SVM classifiers were used to recognize these hand manipulation signal, compared with the independent probabilistic model, the proposed algorithm for the inferred model gain better performance and demonstrate the effectiveness.

机译：在本文中，我们专注于通过手操纵通过测量数据的时间序列对表面肌电信号（sEMG）进行分类的方法。为了表示sEMG的动态特性，基于最大似然估计（MLE）原理在其中包含了随机动态过程。通过使用EM算法，可以轻松识别信号的RMS，WAMP，AR，小波，GMM和HMM特征。收集了十个人，这些人从不同的对象抓取到的不同时间序列数据集具有不同的手握和手部操作。利用BP和SVM分类器对这些手部信号进行识别，与独立的概率模型相比，所提出的推理模型算法具有更好的性能并证明了其有效性。

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《IFAC PapersOnLine》 |2015年第28期|共5页
作者
Yang Lu; Gang Lu; Xianye Bu; Yajing Yu;
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中图分类自动化技术、计算机技术;
关键词
Signal processingPattern RecognitionFeature ExtractionNeural netork;

机译：信号处理模式识别特征提取神经网络;

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Classification of Hand Manipulation Using BP Neural Network and Support Vector Machine Based on Surface Electromyography Signal *

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