基于经验模态分解样本熵的肌电信号识别

doi:10.3969/j.issn.1000-1158.2014.06.03

Abstract
Figure/Table
References (21)
Related Citation (15)

Download: PDF (863 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract According to the chaotic and nonlinear characteristics of surface electromyography(sEMG), a fast and efficient hands movement sEMG pattern recognition method for real-time control of myoelectric prosthetic hand is designed. A multi-modeling pattern recognition method of sEMG features based on the empirical mode decomposition(EMD) sample entropy and clustering analysis is proposed. First, it decomposes the sEMG signal into a set of intrinsic mode functions (IMF), then combines some of the IMF which contains the useful information according to frequency effectiveness, and calculates the sample entropy of the combination.The sample entropy of two sEMG of the extensor carpi ulnaris and flexor carpi ulnaris constitute the feature vector, the clustering classifier which based on principal axis clustering arithmetic is applied to classify the four hand movements. The result shows that four movements (hand extension, hand grasps,wrist spreads and wrist bends) are successfully identified.The average recognition rate is 93%.The method achieved high recognition rate, anti-interference ability and less computation, that is suitable for the control of the myoelectric prosthetic hand.

Key words： Metrology Surface electromyography Empirical mode decomposition Sample entropy Clustering analysis Pattern recognition

Fund:国家自然科学基金(60903084,61172134,61201300);浙江省自然科学基金(LY13F030017);浙江省科技计划项目(2014C33105)

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	XI Xu-gang
	ZHU Hai-gang
	LUO Zhi-zeng
	ZHANG Qi-zhong

Cite this article:

XI Xu-gang,ZHU Hai-gang,LUO Zhi-zeng, et al. EMG Signal Recognition Based on EMD Sample Entropy[J]. Acta Metrologica Sinica, 2014, 35(6): 534-539.

URL:

http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2014.06.03 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2014/V35/I6/534

［1］De Luca C J. Physiology and mathematics of myoelectric signals［J］. IEEE Trans Bio Med Eng, 1979,26(6):313-325.
［2］Hoover C D, Fulk G D, Fite K B. The Design and Initial Experimental Validation of an Active Myoelectric Transfemoral Prosthesis［J］. Journal Medical Devices,2012,6(1):011005-12.
［3］吴小鹰,侯文生,郑小林,等. 腕长伸肌表面肌电与握力大小的相关性研究［J］. 仪器仪表学报,2008,29(8):1605-1608.
［4］高云园,佘青山,孟明,等. 基于多源信息融合的膝上假肢步态识别方法［J］. 仪器仪表学报,2010,31(12):2682-2688.
［5］席旭刚,李仲宁,罗志增. 基于相关性分析和支持向量机的手部肌电信号动作识别［J］. 电子与信息学报,2008,30(10):2315-2319.
［6］张启忠,席旭刚,高发荣.采用匹配滤波和ICA消除sEMG的工频干扰［J］. 计量学报,2013,34（2）：168-172.
［7］Lei M, Wang Z Z, Feng Z J. Detecting nonlinearity of action surface EMG signal［J］.Phys Lett A,2001,290（5－6）:297-303.
［8］刘加海,王丽,王健. 基于相空间、熵和复杂度变化的表面肌电信号分析［J］. 浙江大学学报(理学版),2006,33(2):182-186.
［9］王人成,黄昌华,常宇,等. 表面肌电信号的分形分析［J］. 中国医疗器械杂志, 1999,23(3):125-127.
［10］Hu X, Wang Z Z, Ren X M. Classification of surface EMG signal with fractal dimension［J］.J Zhejiang Univ Sci B,2005,6(8):844-848.
［11］邹晓阳,雷敏. 基于多尺度最大李雅普诺夫指数的表面肌电信号模式识别［J］. 中国生物医学工程学报,2012,31(1):7-12.
［12］Arjunan S P,Kumar D K,Naik G R. Features of sEMG based on source separation and fractal properties to detect wrist movements［J］.Biomed Eng Appl Basis Commun,2010,22(4):293-300.
［13］Naik G R,Kumar D K,Arjunan S P. Towards classification of low-level finger movements using forearm muscle activation: A comparative study based on ICA and Fractal theory［J］.Int J Biomed Eng Technol,2011,6(2):150-162.
［14］Arjunan S P, Kumar D K, Naik G R. A machine learning based method for classification of fractal features of forearm sEMG using twin support vector machines［C］//
Annual International Conference of the IEEE on Engineering in Medicine and Biology Society, Buenos Aires, 2010.
［15］Arjunan S P,Kumar D K,Naik G R. Fractal based modelling and analysis of electromyography to identify subtle actions［C］// Annual International Conference of the IEEE on Engineering in Medicine and Biology Society, Lyon, 2007.
［16］Huang N,Shen Z. The Empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis［J］.Proc R Soc Lond A,1998,454(1971):903-995.
［17］Taheri F. Damage identification in beams using empirical mode decomposition［J］.Struct Health Monit,2011,10(3):261-274.
［18］聂永红,程军圣,张亢,等. 基于EMD与响度的有源噪声控制系统［J］. 仪器仪表学报,2012,33(4):801-808.
［19］Richman J S, Moorman J R. Physiological time-series analysis using approximate entropy and sample entropy［J］.Am J Physiol-Heart and Circulatory Physiol,2000,278(6):H2039-H2049.
［20］王新沛,杨静,李远洋,等. 基于样本熵快速算法的心音信号动力学分析［J］. 振动与冲击,2010,29(11)：115-118.
［21］雷敏,王志中. 一种用于实时提取动作信号的新方法［J］.中国医疗器械杂志,2000,24(4)：200-202.