基于静动态收缩条件的前臂肌电信号疲劳特征研究
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摘要
肌肉在持续收缩过程中会逐渐进入疲劳状态。如何有效地评价肌肉疲劳的程度,对于神经肌肉系统的基础研究、残疾人的康复工程、理疗效果的客观评价和运动员的科学训练等皆有重要的研究意义。表面肌电信号分析由于其方便及非侵入性,且可从整体角度用于分析肌肉的活动,是评价肌肉疲劳的有效工具。本文旨在通过分析肌肉在静动态收缩的过程中表面肌电特征参数的变化,研究肌电信号中的疲劳特征。
研究中设计了静态和动态两种收缩条件下前臂肌肉疲劳的测试平台与实验方案。实验中受试者前臂肌肉握力维持一定水平或呈周期性变化至肌肉疲劳,同时采集其握力和屈肌表面肌电信号;对静态收缩下的表面肌电进行傅里叶变换,获得中值频率和平均频率指标;用连续小波变换分析两种收缩状态下的表面肌电:将小波系数分为高频带和低频带两个部分,以均方根值估计各频带肌电信号的幅值,用以分析在疲劳过程中这些参数的变化趋势。
结果表明,静态收缩条件下肌肉疲劳过程中,中值频率和平均频率指标是疲劳的可靠参数。而小波分析则可用于静态和动态两种收缩状态疲劳肌电的分析。肌电的高频带幅值与肌力水平有关,而低频带幅值则表征了肌疲劳程度。
本文经静、动态两种收缩条件下前臂肌肉疲劳实验和表面肌电信号谱分析研究得到了评价静动态肌肉疲劳的肌电信号特征,尤其是可通过高、低频带肌电信号幅值来分别估计肌力和肌疲劳,为动态肌肉收缩疲劳评价寻找到一种可行的分析方法。
Muscle gets into fatigue during continuous contraction. How to estimate muscle fatigue effectively has an important significance in neuromuscular basic research, rehabilitation engineering, evaluation of the effect of physiotherapy and scientific training of athletes. Surface EMG analysis is an effective tool in muscle fatigue evaluation because that it is convenient, non-invasive, and can be used to analyze the activities of muscle overall. The purpose of this thesis is to analyze the characteristic parameters of sEMG during muscle static and dynamic contractions, and to study sEMG characteristics of muscle fatigue.
In this thesis, forearm muscle fatigue experiments under static and dynamic contractions were performed, and a signal acquisition system was designed. For static contraction, subjects were requested to maintain the force level as steady as possible until exhausted. And in dynamic condition, subjects performed a cyclic and force varying dynamic contraction. The force data were measured from a handgrip dynamometer and sEMG data were recorded from flexor carpi ulnaris. sEMG from static contraction was analyzed with Fourier transform. The Median Frequency and Mean Frequency were calculated as characteristic parameters. SEMG from both contraction conditions were analyzed with Continuous Wavelet Transform using Matlab software. The wavelet coefficients were grouped into high frequency-band (65Hz-350Hz) and low frequency-band (5-45Hz). The amplitude of sEMG signal was determined by calculating the Root Mean Square.
The results show that in static contraction condition, Median Frequency and Mean Frequency are reliable parameters of fatigue. Meanwhile, in both static and dynamic conditions, a correlation is discovered between amplitude of high frequency band and force level. On the other hand, the amplitude of low frequency band is associated with muscle fatigue.
Through the forearm muscle fatigue experiments and spectral analysis of sEMG, the characteristic parameters which can estimate muscle fatigue are acquired. These results have an implication for estimating force and muscle fatigue simultaneously during dynamic contraction.
研究中设计了静态和动态两种收缩条件下前臂肌肉疲劳的测试平台与实验方案。实验中受试者前臂肌肉握力维持一定水平或呈周期性变化至肌肉疲劳,同时采集其握力和屈肌表面肌电信号;对静态收缩下的表面肌电进行傅里叶变换,获得中值频率和平均频率指标;用连续小波变换分析两种收缩状态下的表面肌电:将小波系数分为高频带和低频带两个部分,以均方根值估计各频带肌电信号的幅值,用以分析在疲劳过程中这些参数的变化趋势。
结果表明,静态收缩条件下肌肉疲劳过程中,中值频率和平均频率指标是疲劳的可靠参数。而小波分析则可用于静态和动态两种收缩状态疲劳肌电的分析。肌电的高频带幅值与肌力水平有关,而低频带幅值则表征了肌疲劳程度。
本文经静、动态两种收缩条件下前臂肌肉疲劳实验和表面肌电信号谱分析研究得到了评价静动态肌肉疲劳的肌电信号特征,尤其是可通过高、低频带肌电信号幅值来分别估计肌力和肌疲劳,为动态肌肉收缩疲劳评价寻找到一种可行的分析方法。
Muscle gets into fatigue during continuous contraction. How to estimate muscle fatigue effectively has an important significance in neuromuscular basic research, rehabilitation engineering, evaluation of the effect of physiotherapy and scientific training of athletes. Surface EMG analysis is an effective tool in muscle fatigue evaluation because that it is convenient, non-invasive, and can be used to analyze the activities of muscle overall. The purpose of this thesis is to analyze the characteristic parameters of sEMG during muscle static and dynamic contractions, and to study sEMG characteristics of muscle fatigue.
In this thesis, forearm muscle fatigue experiments under static and dynamic contractions were performed, and a signal acquisition system was designed. For static contraction, subjects were requested to maintain the force level as steady as possible until exhausted. And in dynamic condition, subjects performed a cyclic and force varying dynamic contraction. The force data were measured from a handgrip dynamometer and sEMG data were recorded from flexor carpi ulnaris. sEMG from static contraction was analyzed with Fourier transform. The Median Frequency and Mean Frequency were calculated as characteristic parameters. SEMG from both contraction conditions were analyzed with Continuous Wavelet Transform using Matlab software. The wavelet coefficients were grouped into high frequency-band (65Hz-350Hz) and low frequency-band (5-45Hz). The amplitude of sEMG signal was determined by calculating the Root Mean Square.
The results show that in static contraction condition, Median Frequency and Mean Frequency are reliable parameters of fatigue. Meanwhile, in both static and dynamic conditions, a correlation is discovered between amplitude of high frequency band and force level. On the other hand, the amplitude of low frequency band is associated with muscle fatigue.
Through the forearm muscle fatigue experiments and spectral analysis of sEMG, the characteristic parameters which can estimate muscle fatigue are acquired. These results have an implication for estimating force and muscle fatigue simultaneously during dynamic contraction.
引文
[1]丁海曙,容观澳,王广志,人体运动信息检测与处理,北京:宇航出版社,1992,94~96
[2]牟永阁,基于时频和时间尺度分析的表面肌电信号研究及应用:[博士学位论文],重庆;重庆大学,2004
[3] Duchene J, Goubel F. Surface electromyogram during voluntary contraction: Processing tools and relation to physiological events. Critical Reviews in Biomedical Engineering, 1993,21:313~397
[4] Hagberg M. Electromyographic signs of shoulder muscular fatigue in two elevated arm positions. American Jounral of Physical Medicine, 1981,60:111~121
[5] Stulen F.B, De Luca C.J, Muscle fatigue monitor: A noninvasive device for observing localized muscular fatigue. IEEE Trans Biomed Eng, 1982,29(12): 760~768
[6] Stulen F.B, De Luca C.J, Frequency parameters of the myoelectric signal as a measure of muscle conduction velocity. IEEE Trans Biomed Eng, 1981, 8(7):515
[7] Merletti R, Knaflitz M, De Luca C.J. Myoelectric manifestations of fatigue involuntary and electrically elicited contractions. Journal of Electmmyography and Kinesiology, 1990,69(5):1810
[8]皮喜田,陈峰,彭承琳,利用表面肌电信号评价肌肉疲劳的方法,生物医学工程学杂志,2006,23(1):225~229
[9]温晓利,王健,运动性肌肉疲劳生化机制的研究进展,人类工效学,2005,11(2):60~62
[10]Bonato P, Roy S.H, Knaflitz M, et al. Time-frequency parameters of the surface myoelectric signal for assessing muscle fatigue during cyclic dynamic contractions. IEEE Trans Biomed Eng, 2001,48(7):745
[11]Karlsson S, Yu J, Akay M. Enhancement of spectral analysis of myoelectric signals during static contractions using wavelet methods. IEEE Trans Biomed Eng, 1999,46(9):670
[12]Karlsson S, Yu J, Akay M. Time-frequency analysis of myoelectric signals during dynamic contractions: acomparative study. IEEE Trans Biomed Eng, 2000, 47(2):228
[13]Conforto S, D'Alessio T. Real-time monitoring of muscular fatigue from dynamic surface myoelectric signals using a complex covariance approach. Med. Eng. Physics, 1999,21:225
[14]陈伟婷,王志中,胡晓,等,基于熵的动态收缩sEMG信号疲劳特征分析,中国医学物理学杂志,2006,23(3):204~208
[15]刘磊,岳文治,神经肌电图原理,北京:科学出版社,1983
[16]姚泰,生理学,北京:人民卫生出版社,2004
[17]John V, Basmajian C.J. De Luca, Muscles alive: their functions revealed by electromyography, Williams & Wilkins, 1985
[18]汤晓芙,临床肌电图学,北京:北京医科大学中国协和医科大学联合出版社,1995
[19]穆桂珍,常用电子医疗仪器原理与应用,北京:电子工业出版社,1995
[20]Fitts R.H. Muscle fatigue: The cellular aspects. American Jounral of Sports Medicine, 1996,24:9~13
[21]陈成,肌电信号的处理方法极其对职业性下腰痛评估的研究:[硕士学位论文],天津;天津大学,2003
[22]Chaffin D.B. Localized muscle fatigue: Definition and measurement. Journal of Occupational Medicine, 1973,15:346~354
[23]Hagberg M. Muscular endurance and surface electromyogram in isometric and dynamic exercise. J Appl Physiol, 1981,51:1~7
[24]Avela J, Kyrdlainen H, Komi P.V. Neuormuscular changes after long-lasting mechanically and electrically elicited fatigue. Euorpean Jounral of Applied Physiology, 2001, 85:317-325
[25]赵中华,体育运动中神经—肌肉疲劳研究的新进展,沈阳体育学院学报,2003,3:44~46
[26]Westerblad H, Bruton J.D, Alen D.G, et al. Functional significance of Ca2+ in long-lasting fatigue of skeletal muscle. European Jounral of Applied Physiology, 2000,83:166~174
[27]黄海,运动人体实验学,北京:人民体育出版社,2006.80~182
[28]Duchene J, Goubel F. Surface electromyogram during voluntary contraction: Processing tools and relation to physiological events. Critical Reviews in Biomedical Engineering, 1993,21:313~397
[29]Petrofsky J.S, Glaser R.M, Phillips C.A, et al. Evaluation of the amplitude and frequency components of the surface EMG as an index of muscle faitgue. Ergonomics, 1982,25:213~223
[30]曾志成,系统解剖学,西安:世界图书出版西安公司,2002.73~78
[31]Clancy E.A, Hogan N. Probability density of the surface electromyogram and its relation to amplitude detectors. Bio Eng IEEE Trans, 1999,46(6):730~739
[32]Clancy E.A, Morin E.L, Merletti R. Sampling noise-reduction and amplitude estimation issues in surface electromyography, J Electromyogram Kinesiol, 2002 Feb,12(1):1~16
[33]Karlsson S.B, Gerdle M, Akay. Analyzing surface signals recorded during isokinetic contractions. IEEE Eng Med Bio Mag, 2001,20(6):97~105
[34]徐长发,李国宽,使用小波方法,武汉:华中科技大学出版社,2004.55~81
[35]Sparto P.J, Mohamad P, Barria E.A, et a1. Wavelet and short-time Fourier transform analysis of electromyography for detection of back muscle fatigue. IEEE Transactions on rehabilitation engineering, 2000,8:433~436
[36]Olmo G., Laterza F., Presti L.L. Matched wavelet approach in stretching analysis of dectrically evoked surface EMG signa1. Signal Processing, 2000,80:671~684
[37]Karlsson J.S, Ostlund N, Larsson B, et a1. An estimation of the influence of force decrease on the mean power spectral frequency shift of the EMG during repetitive maximum dynamic knee extensions. Journal of Electmmyography and Kinesiology, 2003,13:46l~468
[38]Yewguan SOO, Masao SUGI, Hiroshi, et al. Simultaneous measurement of force and muscle fatigue using frequency-band wavelet analysis. 30th Annual International IEEE EMBS Conference, 2008
[2]牟永阁,基于时频和时间尺度分析的表面肌电信号研究及应用:[博士学位论文],重庆;重庆大学,2004
[3] Duchene J, Goubel F. Surface electromyogram during voluntary contraction: Processing tools and relation to physiological events. Critical Reviews in Biomedical Engineering, 1993,21:313~397
[4] Hagberg M. Electromyographic signs of shoulder muscular fatigue in two elevated arm positions. American Jounral of Physical Medicine, 1981,60:111~121
[5] Stulen F.B, De Luca C.J, Muscle fatigue monitor: A noninvasive device for observing localized muscular fatigue. IEEE Trans Biomed Eng, 1982,29(12): 760~768
[6] Stulen F.B, De Luca C.J, Frequency parameters of the myoelectric signal as a measure of muscle conduction velocity. IEEE Trans Biomed Eng, 1981, 8(7):515
[7] Merletti R, Knaflitz M, De Luca C.J. Myoelectric manifestations of fatigue involuntary and electrically elicited contractions. Journal of Electmmyography and Kinesiology, 1990,69(5):1810
[8]皮喜田,陈峰,彭承琳,利用表面肌电信号评价肌肉疲劳的方法,生物医学工程学杂志,2006,23(1):225~229
[9]温晓利,王健,运动性肌肉疲劳生化机制的研究进展,人类工效学,2005,11(2):60~62
[10]Bonato P, Roy S.H, Knaflitz M, et al. Time-frequency parameters of the surface myoelectric signal for assessing muscle fatigue during cyclic dynamic contractions. IEEE Trans Biomed Eng, 2001,48(7):745
[11]Karlsson S, Yu J, Akay M. Enhancement of spectral analysis of myoelectric signals during static contractions using wavelet methods. IEEE Trans Biomed Eng, 1999,46(9):670
[12]Karlsson S, Yu J, Akay M. Time-frequency analysis of myoelectric signals during dynamic contractions: acomparative study. IEEE Trans Biomed Eng, 2000, 47(2):228
[13]Conforto S, D'Alessio T. Real-time monitoring of muscular fatigue from dynamic surface myoelectric signals using a complex covariance approach. Med. Eng. Physics, 1999,21:225
[14]陈伟婷,王志中,胡晓,等,基于熵的动态收缩sEMG信号疲劳特征分析,中国医学物理学杂志,2006,23(3):204~208
[15]刘磊,岳文治,神经肌电图原理,北京:科学出版社,1983
[16]姚泰,生理学,北京:人民卫生出版社,2004
[17]John V, Basmajian C.J. De Luca, Muscles alive: their functions revealed by electromyography, Williams & Wilkins, 1985
[18]汤晓芙,临床肌电图学,北京:北京医科大学中国协和医科大学联合出版社,1995
[19]穆桂珍,常用电子医疗仪器原理与应用,北京:电子工业出版社,1995
[20]Fitts R.H. Muscle fatigue: The cellular aspects. American Jounral of Sports Medicine, 1996,24:9~13
[21]陈成,肌电信号的处理方法极其对职业性下腰痛评估的研究:[硕士学位论文],天津;天津大学,2003
[22]Chaffin D.B. Localized muscle fatigue: Definition and measurement. Journal of Occupational Medicine, 1973,15:346~354
[23]Hagberg M. Muscular endurance and surface electromyogram in isometric and dynamic exercise. J Appl Physiol, 1981,51:1~7
[24]Avela J, Kyrdlainen H, Komi P.V. Neuormuscular changes after long-lasting mechanically and electrically elicited fatigue. Euorpean Jounral of Applied Physiology, 2001, 85:317-325
[25]赵中华,体育运动中神经—肌肉疲劳研究的新进展,沈阳体育学院学报,2003,3:44~46
[26]Westerblad H, Bruton J.D, Alen D.G, et al. Functional significance of Ca2+ in long-lasting fatigue of skeletal muscle. European Jounral of Applied Physiology, 2000,83:166~174
[27]黄海,运动人体实验学,北京:人民体育出版社,2006.80~182
[28]Duchene J, Goubel F. Surface electromyogram during voluntary contraction: Processing tools and relation to physiological events. Critical Reviews in Biomedical Engineering, 1993,21:313~397
[29]Petrofsky J.S, Glaser R.M, Phillips C.A, et al. Evaluation of the amplitude and frequency components of the surface EMG as an index of muscle faitgue. Ergonomics, 1982,25:213~223
[30]曾志成,系统解剖学,西安:世界图书出版西安公司,2002.73~78
[31]Clancy E.A, Hogan N. Probability density of the surface electromyogram and its relation to amplitude detectors. Bio Eng IEEE Trans, 1999,46(6):730~739
[32]Clancy E.A, Morin E.L, Merletti R. Sampling noise-reduction and amplitude estimation issues in surface electromyography, J Electromyogram Kinesiol, 2002 Feb,12(1):1~16
[33]Karlsson S.B, Gerdle M, Akay. Analyzing surface signals recorded during isokinetic contractions. IEEE Eng Med Bio Mag, 2001,20(6):97~105
[34]徐长发,李国宽,使用小波方法,武汉:华中科技大学出版社,2004.55~81
[35]Sparto P.J, Mohamad P, Barria E.A, et a1. Wavelet and short-time Fourier transform analysis of electromyography for detection of back muscle fatigue. IEEE Transactions on rehabilitation engineering, 2000,8:433~436
[36]Olmo G., Laterza F., Presti L.L. Matched wavelet approach in stretching analysis of dectrically evoked surface EMG signa1. Signal Processing, 2000,80:671~684
[37]Karlsson J.S, Ostlund N, Larsson B, et a1. An estimation of the influence of force decrease on the mean power spectral frequency shift of the EMG during repetitive maximum dynamic knee extensions. Journal of Electmmyography and Kinesiology, 2003,13:46l~468
[38]Yewguan SOO, Masao SUGI, Hiroshi, et al. Simultaneous measurement of force and muscle fatigue using frequency-band wavelet analysis. 30th Annual International IEEE EMBS Conference, 2008