Gazzoni M, Farina D, Merletti R
Centro di Bioingegneria, Dept. of Electronics, Politecnico di Torino, Italy.
Acta Physiol Pharmacol Bulg. 2001;26(1-2):67-71.
Surface EMG signals were detected from the biceps brachii muscle of five subjects using linear electrode arrays during isometric voluntary contractions at low force levels. A classification method based on neural networks has been used to identify the active motor units during the contraction. In addition, surface EMG global variables have been computed. It was found that at low contraction levels it is possible to reliably identify motor unit action potentials from the interference surface EMG signal and to classify them as belonging to different motor units. Progressive recruitment of new motor units during long duration contractions was observed in all the investigated cases from the first few minutes of contraction (3-4 minutes), indicating a change in the recruitment threshold of non-active motor units as a consequence of muscle fatigue. The recruitment of new motor units was more pronounced for higher force contraction levels than for the lower ones. This behaviour was confirmed in two out of five cases by changes of the global EMG variables.
在低力水平的等长自愿收缩过程中,使用线性电极阵列从五名受试者的肱二头肌检测表面肌电图信号。一种基于神经网络的分类方法已被用于识别收缩过程中活跃的运动单位。此外,还计算了表面肌电图全局变量。结果发现,在低收缩水平下,可以从干扰表面肌电图信号中可靠地识别运动单位动作电位,并将它们分类为属于不同的运动单位。在所有研究案例中,从收缩的最初几分钟(3 - 4分钟)开始,在长时间收缩过程中观察到新运动单位的逐步募集,这表明由于肌肉疲劳,非活跃运动单位的募集阈值发生了变化。对于较高力的收缩水平,新运动单位的募集比低力水平更为明显。在五分之二的案例中,通过全局肌电图变量的变化证实了这种行为。