[1]邵辉,苏芳茵,程海波.采用小波变换和高斯过程的肌电信号模型预测[J].华侨大学学报(自然科学版),2016,37(6):743-748.[doi:10.11830/ISSN.1000-5013.201606016]
　SHAO Hui,SU Fangyin,CHENG Haibo.Model Forecasting of EMG Using Wavelet Transformation and Gaussian Process[J].Journal of Huaqiao University(Natural Science),2016,37(6):743-748.[doi:10.11830/ISSN.1000-5013.201606016]
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采用小波变换和高斯过程的肌电信号模型预测()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第37卷
期数:
2016年第6期
页码:
743-748
栏目:
出版日期:
2016-11-20

文章信息/Info

Title:
Model Forecasting of EMG Using Wavelet Transformation and Gaussian Process
文章编号:
1000-5013(2016)06-0743-06
作者:
邵辉;  苏芳茵;  程海波
华侨大学 信息与科学工程学院, 福建 厦门 361021
Author(s):
SHAO Hui;  SU Fangyin;  CHENG Haibo
College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China
关键词:
表面肌电信号;  高斯过程;  小波变换;  模型预测
Keywords:
surface electromyogram;  Gaussian process;  wavelet transform;  model prediction
分类号:
TP391
DOI:
10.11830/ISSN.1000-5013.201606016
文献标志码:
A
摘要:
根据表面肌电信号的生物电信号特点,采用小波变换和高斯过程建模的方法对表面肌电信号进行建模和预测.对非线性的表面肌电信号利用拟合能力强大的高斯过程进行建模,预测效果较好,但所需运算时间长.针对其运算时间长的缺点进行改进,将预处理后的表面肌电信号小波分解,对分解后的系数高斯建模,然后重构.实验结果表明:该改进方法在响应时间和预测误差方面效果明显.
Abstract:
According to the characteristics of the surface EMG signal, this paper uses wavelet transform and Gauss process modeling method to model and predict the surface EMG signal. The nonlinear surface EMG signal is used to model the fitting ability of the Gauss process, and the prediction effect is better, but the operation time is longer. To overcome the shortcomings of the long computation time, the wavelet decomposition of the surface EMG signal is processed, and the coefficients of the decomposition are modeled in Guassian. Experimental results show that the improved method has obvious effect on response time and prediction error.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2016-01-14
通信作者: 邵辉(1973-),女,副教授,博士,主要从事机器人控制、运动规划、智能控制、非线性系统LPV建模的研究.E-mail:shaohuihull@163.com.
基金项目: 福建省科技计划项目(2015H0026); 教育部留学回国人员科研启动基金资助项目(Z1534004); 福建省泉州市科技计划项目(2013Z34)
更新日期/Last Update: 2016-11-20