Al Harrach Mariam, Carriou Vincent, Boudaoud Sofiane, Laforet Jeremy, Marin Frederic
Sorbonne Universites, Universite de Technologie de Compiegne, UMR CNRS 7338 Biomecanique et Bioingenieurie (BMBI), Centre de recherche Royallieu, CS 60203 Compiegne cedex, France.
Sorbonne Universites, Universite de Technologie de Compiegne, UMR CNRS 7338 Biomecanique et Bioingenieurie (BMBI), Centre de recherche Royallieu, CS 60203 Compiegne cedex, France.
Comput Biol Med. 2017 Apr 1;83:34-47. doi: 10.1016/j.compbiomed.2017.02.003. Epub 2017 Feb 16.
The relationship between the surface Electromyogram (sEMG) signal and the force of an individual muscle is still ambiguous due to the complexity of experimental evaluation. However, understanding this relationship should be useful for the assessment of neuromuscular system in healthy and pathological contexts. In this study, we present a global investigation of the factors governing the shape of this relationship. Accordingly, we conducted a focused sensitivity analysis of the sEMG/force relationship form with respect to neural, functional and physiological parameters variation. For this purpose, we used a fast generation cylindrical model for the simulation of an 8×8 High Density-sEMG (HD-sEMG) grid and a twitch based force model for the muscle force generation. The HD-sEMG signals as well as the corresponding force signals were simulated in isometric non-fatiguing conditions and were based on the Biceps Brachii (BB) muscle properties. A total of 10 isometric constant contractions of 5s were simulated for each configuration of parameters. The Root Mean Squared (RMS) value was computed in order to quantify the sEMG amplitude. Then, an image segmentation method was used for data fusion of the 8×8 RMS maps. In addition, a comparative study between recent modeling propositions and the model proposed in this study is presented. The evaluation was made by computing the Normalized Root Mean Squared Error (NRMSE) of their fitting to the simulated relationship functions. Our results indicated that the relationship between the RMS (mV) and muscle force (N) can be modeled using a 3rd degree polynomial equation. Moreover, it appears that the obtained coefficients are patient-specific and dependent on physiological, anatomical and neural parameters.
由于实验评估的复杂性，表面肌电图（sEMG）信号与单个肌肉力量之间的关系仍不明确。然而，了解这种关系对于在健康和病理情况下评估神经肌肉系统应该是有用的。在本研究中，我们对控制这种关系形状的因素进行了全面调查。因此，我们针对神经、功能和生理参数变化，对sEMG/力量关系形式进行了重点敏感性分析。为此，我们使用了一个快速生成的圆柱形模型来模拟8×8高密度表面肌电图（HD-sEMG）网格，并使用了一个基于抽搐的力量模型来生成肌肉力量。HD-sEMG信号以及相应的力量信号在等长非疲劳条件下进行模拟，并基于肱二头肌（BB）的肌肉特性。对于每种参数配置，总共模拟了10次持续5秒的等长恒定收缩。计算均方根（RMS）值以量化sEMG幅度。然后，使用图像分割方法对8×8 RMS图进行数据融合。此外，还对最近的建模命题与本研究中提出的模型进行了比较研究。通过计算它们与模拟关系函数拟合的归一化均方根误差（NRMSE）来进行评估。我们的结果表明，RMS（mV）与肌肉力量（N）之间的关系可以用三次多项式方程建模。此外，所获得的系数似乎是特定于患者的，并且取决于生理、解剖和神经参数。
