Estimation of the interplay between groups of fast and slow muscle fibers of the tibialis anterior and gastrocnemius muscle while running

被引:56
作者
Von Tscharner, V
Goepfert, B
机构
[1] Univ Calgary, Fac Kinesiol, Human Performance Lab, Calgary, AB T2N 1N4, Canada
[2] Univ Basel, Labor Orthopaed Biomech, CH-4003 Basel, Switzerland
关键词
wavelet analysis; time frequency analysis; spectral decomposition;
D O I
10.1016/j.jelekin.2005.07.004
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Electromyograms recorded from the lower limbs of humans while running were submitted to a time/frequency analysis using wavelets. The results of the wavelet analysis yielded intensity spectra at every time point during the swing and the stance phase. It was previously shown that more or less high frequency components get activated during different periods of the movement. The purpose of this study was to test to what extent the spectra can be reconstructed by a linear superposition of two generating spectra that were associated to groups of fast and slow muscle fibers. The terms fast and slow do not only refer to the conduction velocity but also to the shape of the motor unit action potential and are used to characterize the groups in a broader sense. The principal component analysis of the spectra confirmed that a two dimensional spectral space was appropriate. A parametric spectral decomposition was used to extract the generating spectra within the two dimensional spectral space. The generating spectra were in turn used to compute the power with which the groups of muscle fibers contribute to the measured spectra and thus to the overall muscular activity. The power that was obtained for the different time points during the movement reflects the biomechanically important interplay between the groups of muscle fibers while running. (C) 2005 Elsevier Ltd. All rights reserved.
引用
收藏
页码:188 / 197
页数:10
相关论文
共 26 条
[1]   MUSCLE FIBER TYPES - HOW MANY AND WHAT KIND [J].
BROOKE, MH ;
KAISER, KK .
ARCHIVES OF NEUROLOGY, 1970, 23 (04) :369-&
[2]   Optimal rejection of movement artefacts from myoelectric signals by means of a wavelet filtering procedure [J].
Conforto, S ;
D'Alessio, T ;
Pignatelli, S .
JOURNAL OF ELECTROMYOGRAPHY AND KINESIOLOGY, 1999, 9 (01) :47-57
[3]   Interpretation of EMG changes with fatigue: facts, pitfalls, and fallacies [J].
Dimitrova, NA ;
Dimitrov, GV .
JOURNAL OF ELECTROMYOGRAPHY AND KINESIOLOGY, 2003, 13 (01) :13-36
[4]  
Doud J R, 1995, Electromyogr Clin Neurophysiol, V35, P331
[5]   The extraction of neural strategies from the surface EMG [J].
Farina, D ;
Merletti, R ;
Enoka, RM .
JOURNAL OF APPLIED PHYSIOLOGY, 2004, 96 (04) :1486-1495
[6]   The influences of muscle fibre proportions and areas upon EMG during maximal dynamic knee extensions [J].
Gerdle, B ;
Karlsson, S ;
Crenshaw, AG ;
Elert, J ;
Fridén, J .
EUROPEAN JOURNAL OF APPLIED PHYSIOLOGY AND OCCUPATIONAL PHYSIOLOGY, 2000, 81 (1-2) :2-10
[7]   FINITE LIMB DIMENSIONS AND FINITE MUSCLE LENGTH IN A MODEL FOR THE GENERATION OF ELECTROMYOGRAPHIC SIGNALS [J].
GOOTZEN, THJM ;
STEGEMAN, DF ;
VANOOSTEROM, A .
ELECTROENCEPHALOGRAPHY AND CLINICAL NEUROPHYSIOLOGY, 1991, 81 (02) :152-162
[8]   Time-frequency analysis of myoelectric signals during dynamic contractions: A comparative study [J].
Karlsson, S ;
Yu, J ;
Akay, M .
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2000, 47 (02) :228-238
[9]   Mean frequency and signal amplitude of the surface EMG of the quadriceps muscles increase with increasing torque - a study using the continuous wavelet transform [J].
Karlsson, S ;
Gerdle, B .
JOURNAL OF ELECTROMYOGRAPHY AND KINESIOLOGY, 2001, 11 (02) :131-140
[10]   Time-frequency methods applied to muscle fatigue assessment during dynamic contractions [J].
Knaflitz, M ;
Bonato, P .
JOURNAL OF ELECTROMYOGRAPHY AND KINESIOLOGY, 1999, 9 (05) :337-350