Del Vecchio Alessandro, Bazzucchi Ilenia, Felici Francesco
Neuromuscular Research Centre & Technology, Department of Bioengineering, Imperial College London, SW7 2AZ London, UK; Department of Movement, Human and Health Sciences, University of Rome "Foro Italico", 00135 Rome, Italy.
Department of Movement, Human and Health Sciences, University of Rome "Foro Italico", 00135 Rome, Italy.
J Electromyogr Kinesiol. 2018 Jun;40:102-109. doi: 10.1016/j.jelekin.2018.04.010. Epub 2018 Apr 24.
The force developed by the human neuromuscular system can change very rapidly (15-50 ms). When processing EMG signals for inferring neural control strategies, it is therefore necessary to extract estimates from short time intervals. In this study, we investigate the relation between joint torque and estimates of average muscle fibre conduction velocity (MFCV) and amplitude (RMS) from surface EMG signals, when varying the duration of the processing interval. Moreover, we assessed the inter-subject variability in RMS and MFCV estimates. Ten healthy subjects performed isometric linearly increasing ankle dorsiflexion contractions up to 70% MVC at a rate of 5%MVCs. High-density EMG signals were recorded from the tibialis anterior muscle and MFCV and RMS were estimated in eight time-intervals ranging from 15 to 2000 ms. MFCV and RMS were significantly correlated with force in all subjects and when using all time-intervals (MFCV = 0.77 ± 0.07, RMS = 0.79 ± 0.06 (R), Pearson-P < 0.01). The variability around the regression line for both MFCV and RMS estimates significantly increased when using intervals <100 ms (P < 0.001). However, the slope of the regression between EMG variables and force did not change with the duration of the interval (P < 0.001). Moreover, MFCV showed a substantially smaller variability across subjects in its relation to force than RMS [average coefficient of variation of regression slopes across all time intervals, 24.48 ± 1.51 (%), whilst for the RMS it was 56.65 ± 0.69 (%)]. These results indicate that estimates of MFCV and RMS as a function of joint torque are unbiased with respect to processing interval duration. Moreover, they reveal that estimates of MFCV are more consistent across subjects than EMG amplitude.
人类神经肌肉系统产生的力量能够非常迅速地变化（15 - 50毫秒）。因此，在处理肌电图（EMG）信号以推断神经控制策略时，有必要从短时间间隔中提取估计值。在本研究中，我们探究了在改变处理间隔持续时间时，关节扭矩与表面肌电图信号的平均肌纤维传导速度（MFCV）和幅度（均方根值，RMS）估计值之间的关系。此外，我们评估了均方根值和平均肌纤维传导速度估计值在不同受试者之间的变异性。十名健康受试者以5%最大随意收缩（MVC）的速率进行等长线性增加的踝关节背屈收缩，直至达到70%MVC。从胫骨前肌记录高密度肌电图信号，并在15至2000毫秒的八个时间间隔内估计平均肌纤维传导速度和均方根值。在所有受试者中，以及使用所有时间间隔时，平均肌纤维传导速度和均方根值与力量显著相关（平均肌纤维传导速度 = 0.77 ± 0.07，均方根值 = 0.79 ± 0.06（相关系数，R），皮尔逊相关系数P < 0.01）。当使用小于100毫秒的间隔时，平均肌纤维传导速度和均方根值估计值围绕回归线的变异性显著增加（P < 0.001）。然而，肌电图变量与力量之间回归的斜率并未随间隔持续时间而变化（P < 0.001）。此外，平均肌纤维传导速度在其与力量的关系中，在不同受试者之间显示出比均方根值小得多的变异性[所有时间间隔内回归斜率的平均变异系数，24.48 ± 1.51（%），而均方根值为56.65 ± 0.69（%）]。这些结果表明，作为关节扭矩函数的平均肌纤维传导速度和均方根值估计值在处理间隔持续时间方面是无偏的。此外，它们揭示出平均肌纤维传导速度估计值在不同受试者之间比肌电图幅度更一致。
