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...
科学通报  2014 

基于sEMG振子模型的骨骼肌等长收缩力与固有特性的能量核表征方法

DOI: 10.1360/csb2014-59-7-561, PP. 561-571

Keywords: 表贴EMG信号,EMG振子,能量核,等长收缩力,自然频率

Full-Text   Cite this paper   Add to My Lib

Abstract:

提出了一种肌肉等长收缩力估计与肌肉固有特性表征的新方法,称为能量核方法.此方法的初衷在于将表贴EMG(肌电图)信号转变为平面内的相图,并将相图上状态点的分布核心称作能量核,而噪声信号的分布核心称为噪声核.基于相图的统计特征,将一段EMG信号近似为简谐振子,简称EMG振子.本文建立了控制信号(EMG)与输出信号(力/功率)之间的关系,并提出用EMG的特征能量来表征肌肉力.另一方面,通过对能量核与噪声核的计算,能够得到噪声与EMG信号的自然频率并实现直观的信噪识别与分离.实验结果表明,特征能量对等长收缩力的表征度令人满意,并且由于结合了RMS与MPF两种方法的优点,此方法具有很高的鲁棒性;而特定肌肉的EMG自然频率不取决于MU放电频率,故其反映了肌肉的固有特性.此模型体现的物理意义为EMG信号的理解与分析提供了新的启发.

References

[1]  1 Staudenmann D, Roeleveld K, Stegeman D F, et al. Methodological aspects of SEMG recordings for force estimation—A tutorial and review. J Electromyopr Kinesiol, 2010, 20: 375-387
[2]  2 Bigland-Ritchie B, Donovan E F, Roussos C S. Conduction velocity and EMG power spectrum changes in fatigue of sustained maximal efforts. J Appl Physiol, 1981, 51: 1300-1305
[3]  5 Güler N F, Ko?er S. Classification of EMG signals using PCA and FFT. J Med Syst, 2005, 29: 241-250
[4]  6 Talebinejad M, Chan A D C, Miri A, et al. Fractal analysis of surface electromyography signals: A novel power spectrum-based method. J Electromyopr Kinesiol, 2009, 19: 840-850
[5]  9 Gabor D. Theory of communication. J Inst Electr Eng, 1946, 93: 429-457
[6]  12 Michele G D, Sello S, Garboncini M C, et al. Cross-correlation time-frequency analysis for multiple EMG signals in Parkinson's disease: A wavelet approach. Med Eng Phys, 2003, 25: 361-369
[7]  13 张清菊, 罗志增, 叶明. 基于功率谱分析和RBF网络的表面EMG多模式分类. 机电工程, 2005, 22: 35-38
[8]  16 Holobar A, Zazula D. Multi-channel blind source separation using convolution kernel compensation. IEEE Trans signal process, 2007, 8: 4487-4496
[9]  17 Azzerboni B, Finocchio G, Ipsale M, et al. A new approach to detection of muscle activation by independent component analysis and wavelet transform. Comput Sci, 2002, 2486: 109-116
[10]  20 殷跃红, 郭朝, 陈幸, 等. 基于分子马达运行机制的骨骼肌生物力学原理研究进展. 科学通报, 2012, 57: 2794-2805
[11]  21 Guo Z, Fan Y J, Zhang J J, et al. A new 4M model-based human-machine interface for lower extremity exoskeleton robot. In: The 5th International Conference on Intelligent Robotics and Applications, 2012, October 3-5, Montreal, Canada. Heidelberg: Springer, 2012. 123-130
[12]  22 Gabriel D A, Christie A, Inglis J G, et al. Experimental and modeling investigation of surface EMG spike analysis. Med Eng Phys, 2011, 33: 427-437
[13]  23 Merletti R, Conte L L, Avignone E, et al. Modeling of surface myoelectric signals. Part I: Model implementation. IEEE Trans Biomed Eng, 1999, 46: 810-820
[14]  26 McComas A J, Mrozek K, Gardner-Medwin D, et al. Electrical properties of muscle fibre membranes in man. J Neurol Neurosurg Phychiat, 1968, 31: 434-440
[15]  27 殷跃红, 陈幸. 骨骼肌收缩的生物电化学变频调控原理——基于分子马达运行机制的骨骼肌生物力学原理(Ⅱ). 中国科学: 技术科学, 2012, 42: 901-910
[16]  32 Zhou S, Lawson D L, Morrison W E. Electromechanical delay in isometric muscle contractions evoked by voluntary, reflex and electrical stimulation. Eur J Appl Physiol, 1995, 70: 138-145
[17]  35 Lowery M M, Stoykov N S, Dewald J P A, et al. Volume conduction in an anatomically based surface EMG model. IEEE Trans Biomed Eng, 2004, 51: 2138-2147
[18]  3 Mannion A F, Connolly B, Wood K, et al. The use of surface EMG power spectral analysis in the evaluation of back muscle function. J Rehabil Res Dev, 1997, 34: 427-439
[19]  4 Komi P V, Tesch P. EMG frequency spectrum, muscle structure, and fatigue during dynamic contractions in man. Eur J Appl Physiol Occup Physiol, 1979, 42: 41-50
[20]  7 Christie A, Inglis G, Kamen G. Relationships between surface EMG variables and motor unit firing rates. Eur J Appl Physiol, 2009, 107: 177-185
[21]  8 Qi L, Wakeling J M, Green A, et al. Spectral properties of electromyographic and mechanomyographic signals during isometric ramp and step contractions in biceps brachii. J Electromyopr Kinesiol, 2011, 21: 128-135
[22]  10 Claasen T, Mecklenbrauker W. The Wigner distribution—A tool for time-frequency analysis. Part Ⅰ: continuous-time signals. Phylips J Res, 1980, 35: 217-250
[23]  11 Wang G, Wang Z Z, Chen W T, et al. Classification of surface EMG signals using optimal wavelet packet method based on Davies-Bouldin criterion. Med Bio Eng Comput, 2006, 44: 865-872
[24]  14 Kukolj D, Levi E. Identification of complex systems based on neural and Takagi-Sugeno fuzzy model. IEEE Trans Syst Man Cybern B, 2003, 34: 272-282
[25]  15 Vineet G, Srikanth S, Narender P R. Fractal analysis of surface EMG signals from the biceps. Int J Med Inform, 1997, 45: 185-192
[26]  18 Nair S S, French R M, Laroche D, et al. The application of machine learning algorithms to the analysis of electromyographic patterns from arthritic patients. IEEE Trans Neural Syst Rehabil Eng, 2010, 18: 174-184
[27]  19 Levi J H, Erik J S, Kevin B E, et al. Multiple binary classifications via linear discriminant analysis for improved controllability of a powered prosthesis. IEEE Trans Neural Syst Rehabil Eng, 2010, 18: 49-57
[28]  24 Day S J, Hulliger M. Experimental simulation of cat electromyogram: Evidence for algebraic summation of motor-unit action-potential trains. J Neurophysiol, 2001, 86: 2144-2158
[29]  25 杨基海, 杨洪宁. 具有迭加动作电位波形的EMG信号自动分解研究. 中国生物医学工程学报, 1999, 18: 82-88
[30]  28 Kesar T, Chou L W, Binder-Macleod S A. Effects of stimulation frequency versus pulse duration modulation on muscle fatigue. J Electromyopr Kinesiol, 2008, 18: 662-671
[31]  29 Yin Y H, Fan Y J, Xu L D. EMG & EPP-integrated human-machine interface between the paralyzed and rehabilitation exoskeleton. IEEE Trans Inf Tech Biomed, 2012, 16: 542-549
[32]  30 Yin Y H, Fan Y J, Guo Z. sEMG-based neuro-fuzzy controller for a parallel ankle exoskeleton with proprioception. Int J Robot Autom, 2011, 26: 450-460
[33]  31 Fan Y J, Yin Y H. Differentiated time-frequency characteristics based real-time human-machine interface for lower extremity rehabilitation exoskeleton robot. In: The 5th International Conference on Intelligent Robotics and Applications, 2012, October 3-5, Montreal, Canada. Heidelberg: Springer, 2012. 31-40
[34]  33 Rasmussen J, Damsgaard M, Voigt M. Muscle recruitment by the min/max criterion-a comparative numerical study. J Biomech, 2001, 34: 409-415
[35]  34 周前祥, 谌玉红, 马超, 等. 基于sEMG信号的操作者上肢肌肉施力疲劳评价模型研究. 中国科学: 生命科学, 2011, 41: 608-614
[36]  36 Ren Q, Zhao Y P, Yue J C, et al. Biological application of multi-component nanowires in hybrid devices powered by F1-ATPase motors. Biomed Microdev, 2006, 8: 201-208
[37]  37 郭朝, 殷跃红. 基于分子马达集体运行机制的骨骼肌收缩动态力学模型——基于分子马达运行机制的骨骼肌生物力学原理(Ⅰ). 中国科学: 技术科学, 2012, 42: 672-679
[38]  38 陈幸, 殷跃红. 肌梭传入神经主动突触后反应的动力系统——Markov模型. 科学通报, 2012, 58: 793-802

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133