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[1]石军梅,王从庆△,左超.基于改进人工蜂群优化与组合特征提取的手部运动意图识别*[J].生物医学工程研究,2018,04:481-486.
 SHI Junmei,WANG Congqing,ZUO Chao.Hand movement intention recognition based on improved artificial bee colong optimization and combined feature extraction[J].Journal of Biomedical Engineering Research,2018,04:481-486.
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基于改进人工蜂群优化与组合特征提取的手部运动意图识别*(PDF)

《生物医学工程研究》[ISSN:1006-6977/CN:61-1281/TN]

期数:
2018年04期
页码:
481-486
栏目:
出版日期:
2018-12-25

文章信息/Info

Title:
Hand movement intention recognition based on improved artificial bee colong optimization and combined feature extraction
文章编号:
1672-6278 (2018)04-0481-06
作者:
石军梅王从庆△左超
南京航空航天大学自动化学院,南京 210016
Author(s):
SHI JunmeiWANG CongqingZUO Chao
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
关键词:
改进人工蜂群优化四阶累积量有序盲源分离组合特征提取手部运动意图识别
Keywords:
Improved artificial bee colony optimizationNormalized fourth-order cumulantSequential blind separation Combined featureHand movement intention recognition
分类号:
R318;TH77
DOI:
10.19529/j.cnki.1672-6278.2018.04.20
文献标识码:
A
摘要:
为了解决表面肌电信号混迭导致的手部运动意图识别率较低的问题,提出了一种基于改进的人工蜂群优化盲源有序分离算法。本算法以表面肌电信号的规范四阶累积量作为代价函数,使用改进的人工蜂群优化算法代替传统的梯度算法对代价函数进行优化,并以代价函数绝对值的降序逐次提取出源信号;对于肌电信号的非平稳性及易受干扰的问题,采用一种基于小波包变换和样本熵的特征提取方法,并与表征肌电信号细节和强度的特征峰度、偏度、肌电积分值组合构建特征向量,训练二叉树支持向量机分类器。实验结果表明,采用表面肌电信号的盲源分离预处理与组合特征提取的方法识别六种手部运动意图,平均准确率达到93.33%。
Abstract:
A sequential blind source signal separation algorithm based on Improved Artificial Bee Colony Optimization(IABO) algorithm was proposed to solve the problem of low recognition accuracy of hand movement intention,which caused by aliasing of surface electromyography(sEMG) signal. Normalized fourth-order cumulant of the sEMG signal was used as cost function in the algorithm,and traditional gradient algorithm was replaced by IABO for optimizing the cost function.Therefore,the source signal could be extracted on the descending order of normalized fourth-order cumulant;Considering the characteristics of non-stationary and easily disturbed of sEMG signal,we proposed a combined feature extraction method,which contained sample entropy from the subspace of wavelet packet decomposition and kurtosis、skewness、the integral values of sEMG signal .At last,binary tree support vector machine classifier with combined features was trained.The experimental results show that,six kinds of hand movements intention can be effectively predicted by using the blind source separation and combined feature, the average accuracy rate reaches to 93.33% .

参考文献/References

备注/Memo

备注/Memo:
(收稿日期:2018-03-16) 江苏省重点研发计划(BE2016757)。△通信作者Email:cqwang@nuaa.edu.cn
更新日期/Last Update: 2019-01-30