全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于自排序熵的表面肌电信号特征提取方法*

, PP. 496-501

Keywords: 表面肌电信号(sEMG),自排序熵,特征提取,支持向量机

Full-Text   Cite this paper   Add to My Lib

Abstract:

针对人体表面肌电信号的非平稳、非线性特点,提出一种基于排序熵和自互信息的自排序熵指标,定量描述表面肌电信号的内在动力学特性,实现肢体不同运动状态下肌电信号非线性特征的有效刻画.进行健康受试者上肢肘关节不同弯曲角度下表面肌电采集实验,计算其自排序熵指标并运用支持向量机进行动作识别,通过与已有表面肌电特征指标的对比分析,验证文中方法的有效性.

References

[1]  Bu N, Okamoto M, Tsuji T. A Hybrid Motion Classification Approach for EMG-Based Human-Robot Interfaces Using Bayesian and Neural Networks. IEEE Trans on Robotics, 2009, 25(3): 502-511
[2]  Li N, Jiang L, Wang X Q, et al. Design of a 5-DOF Prosthetic Hand with Grasping Force Sensory Feedback. High Technology Letters, 2011, 21(4): 392-397 (in Chinese)(李 楠,姜 力,王新庆,等.具有握力感觉反馈功能的五自由度假手系统设计.高技术通讯, 2011, 21(4): 392-397)
[3]  Maeshima S, Osawa A, Nishio D, et al. Efficacy of a Hybrid Assistive Limb in Post-Stroke Hemiplegic Patients: A Preliminary Report. BMC Neurology, 2011.DOI: 10.1186/1471-2377-11-116
[4]  Khokhar Z O, Xiao Z G, Menon C. Surface EMG Pattern Recognition for Real-Time Control of a Wrist Exoskeleton [EB/OL].[2013-02-10]. http:// link.springer.com/article/10.1186%2F1475-925X-9-41
[5]  Luo Z Z, Ma W J, Meng M. Pattern Recognition of Hand Motions Based on HHT and AR-Model. Pattern Recognition and Artificial Intelligence, 2008, 21(2): 227-232 (in Chinese)(罗志增,马文杰,孟 明.一种基于HHT和AR模型的手部运动模式识别方法.模式识别与人工智能, 2008, 21(2): 227-232)
[6]  Zhang X, Zhou P. Sample Entropy Analysis of Surface EMG for Improved Muscle Activity Onset Detection against Spurious Background Spikes. Journal of Electromyography and Kinesiology, 2012, 22(6): 901-907
[7]  Popovic M R, Keller T, Papas I P I, et al. Surface-Stimulation Technology for Grasping and Walking Neuroprostheses. IEEE Engineering in Medicine and Biology Magazine, 2001, 20(1): 82-93
[8]  Cao Y H, Tung W W, Gao J B, et al. Detecting Dynamical Changes in Time Series Using the Permutation Entropy[EB/OL]. [2013-05-01]. http://web.ics.purdue.edu/~wwtung/2004_CaoTungGaoPH.pdf
[9]  Taherkhani F, Rahmani M, Taherkhani F, et al. Permutation Entropy and Detrend Fluctuation Analysis for the Natural Complexity of Cardiac Heart Interbeat Signals. Physica A: Statistical Mechanics and Its Applications, 2013, 392(14): 3106-3112
[10]  Li X L, Ouyang G X. Estimating Coupling Direction between Neuronal Populations with Permutation Conditional Mutual Information. NeuroImage, 2010, 52(2): 497-507
[11]  Yan Z G, Wang Z Z, Ren X M. The Application of the ICA and the Wavelet Entropy in Motion Recognition. Beijing Biomedical Engineering, 2006, 25(5): 457-465 (in Chinese)(颜志国,王志中,任晓梅.独立分量分析和小波熵在动作模式分类中的应用.北京生物医学工程, 2006, 25(5): 457-465)
[12]  Hoyer D, Frank B, Pompe B, et al. Analysis of Complex Physiological Systems by Information Flow: A Time Scale-Specific Complexity Assessment. Biomedical Engineering, 2006, 51(2): 41-48
[13]  Meng M, Luo Z Z. Hand Motion Classification Based on Eye-Moving Assisted EEG. Pattern Recognition and Artificial Intelligence, 2012, 25(6): 1007-1012 (in Chinese)(孟 明,罗志增.基于眼动辅助脑电信号的手部动作分类方法.模式识别与人工智能, 2012, 25(6): 1007-1012)

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133