Sorbonne Universities, Universite de Technologie de Compiegne, CNRS UMR 7338 Biomechanics and Bioengineering, Centre de recherche Royallieu, CS 60203 Compiegne cedex, France.
Sorbonne Universities, Universite de Technologie de Compiegne, CNRS UMR 7338 Biomechanics and Bioengineering, Centre de recherche Royallieu, CS 60203 Compiegne cedex, France.
Comput Biol Med. 2017 Oct 1;89:44-58. doi: 10.1016/j.compbiomed.2017.07.023. Epub 2017 Aug 1.
This work presents an evaluation of the High Density surface Electromyogram (HD-sEMG) Probability Density Function (PDF) shape variation according to contraction level. On that account, using PDF shape descriptors: High Order Statistics (HOS) and Shape Distances (SD), we try to address the absence of a consensus for the sEMG non-Gaussianity evolution with force variation. This is motivated by the fact that PDF shape information are relevant in physiological assessment of the muscle architecture and function, such as contraction level classification, in complement to classical amplitude parameters. Accordingly, both experimental and simulation studies are presented in this work. For data fusion, the watershed image processing technique was used. This technique allowed us to find the dominant PDF shape variation profiles from the 64 signals. The experimental protocol consisted of three isometric isotonic contractions of 30, 50 and 70% of the Maximum Voluntary Contraction (MVC). This protocol was performed by six subjects and recorded using an 8 × 8 HD-sEMG grid. For the simulation study, the muscle modeling was done using a fast computing cylindrical HD-sEMG generation model. This model was personalized by morphological parameters obtained by sonography. Moreover, a set of the model parameter configurations were compared as a focused sensitivity analysis of the PDF shape variation. Further, monopolar, bipolar and Laplacian electrode configurations were investigated in both experimental and simulation studies. Results indicated that sEMG PDF shape variations according to force increase are mainly dependent on the Motor Unit (MU) spatial recruitment strategy, the MU type distribution within the muscle, and the used electrode arrangement. Consequently, these statistics can give us an insight into non measurable parameters and specifications of the studied muscle primarily the MU type distribution.
本工作评估了高密度表面肌电图（HD-sEMG）概率密度函数（PDF）形状随收缩水平的变化。为此，我们使用 PDF 形状描述符：高阶统计量（HOS）和形状距离（SD），尝试解决 sEMG 非高斯性随力变化的缺乏共识问题。这是因为 PDF 形状信息与肌肉结构和功能的生理评估相关，例如收缩水平分类，补充了经典的幅度参数。因此，本工作同时进行了实验和模拟研究。对于数据融合，使用了分水岭图像处理技术。该技术允许我们从 64 个信号中找到主导 PDF 形状变化的轮廓。实验方案包括三个等长等张收缩，分别为最大随意收缩（MVC）的 30%、50%和 70%。该方案由六名受试者完成，并使用 8×8 HD-sEMG 网格进行记录。对于模拟研究，使用快速计算的圆柱形 HD-sEMG 生成模型进行肌肉建模。该模型通过超声获得的形态参数进行个性化设置。此外，还对一组模型参数配置进行了比较，作为 PDF 形状变化的集中敏感性分析。进一步，在实验和模拟研究中都研究了单极、双极和拉普拉斯电极配置。结果表明，随着力的增加，sEMG PDF 形状的变化主要取决于运动单位（MU）的空间募集策略、肌肉内 MU 类型的分布以及所使用的电极排列。因此，这些统计数据可以使我们深入了解研究肌肉的不可测量参数和特性，主要是 MU 类型的分布。
