Rao Guillaume, Berton Eric, Amarantini David, Vigouroux Laurent, Buchanan Thomas S
Institute of Movement Sciences, University of the Mediterranean, UMR CNRS 6233, 163, Avenue de Luminy, 13288 Marseille Cedex 09, France.
J Biomech Eng. 2010 Jul;132(7):071003. doi: 10.1115/1.4001383.
Although it is well known that fatigue can greatly reduce muscle forces, it is not generally included in biomechanical models. The aim of the present study was to develop an electromyographic-driven (EMG-driven) biomechanical model to estimate the contributions of flexor and extensor muscle groups to the net joint moment during a nonisokinetic functional movement (squat exercise) performed in nonfatigued and in fatigued conditions. A methodology that aims at balancing the decreased muscle moment production capacity following fatigue was developed. During an isometric fatigue session, a linear regression was created linking the decrease in force production capacity of the muscle (normalized force/EMG ratio) to the EMG mean frequency. Using the decrease in mean frequency estimated through wavelet transforms between dynamic squats performed before and after the fatigue session as input to the previous linear regression, a coefficient accounting for the presence of fatigue in the quadriceps group was computed. This coefficient was used to constrain the moment production capacity of the fatigued muscle group within an EMG-driven optimization model dedicated to estimate the contributions of the knee flexor and extensor muscle groups to the net joint moment. During squats, our results showed significant increases in the EMG amplitudes with fatigue (+23.27% in average) while the outputs of the EMG-driven model were similar. The modifications of the EMG amplitudes following fatigue were successfully taken into account while estimating the contributions of the flexor and extensor muscle groups to the net joint moment. These results demonstrated that the new procedure was able to estimate the decrease in moment production capacity of the fatigued muscle group.
虽然众所周知疲劳会大幅降低肌肉力量,但它通常未被纳入生物力学模型。本研究的目的是开发一种肌电图驱动(EMG驱动)的生物力学模型,以估计在非疲劳和疲劳状态下进行的非等速功能性运动(深蹲运动)过程中,屈肌和伸肌肌群对净关节力矩的贡献。开发了一种旨在平衡疲劳后肌肉力矩产生能力下降的方法。在等长疲劳训练期间,建立了线性回归,将肌肉力量产生能力的下降(归一化力/EMG比率)与EMG平均频率联系起来。使用通过小波变换估计的疲劳训练前后进行的动态深蹲之间的平均频率下降作为先前线性回归的输入,计算出一个考虑股四头肌组疲劳存在的系数。该系数用于在一个专门用于估计膝关节屈肌和伸肌肌群对净关节力矩贡献的EMG驱动优化模型中,限制疲劳肌肉群的力矩产生能力。在深蹲过程中,我们的结果显示,随着疲劳,EMG幅度显著增加(平均增加23.27%),而EMG驱动模型的输出相似。在估计屈肌和伸肌肌群对净关节力矩的贡献时,成功考虑了疲劳后EMG幅度的变化。这些结果表明,新方法能够估计疲劳肌肉群力矩产生能力的下降。