利用非线性相关精确评估HD-sEMG网格错位

R. Khoury, Ahmad Diab, V. Carriou, J. Laforêt, S. Boudaoud
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引用次数: 0

摘要

本文的目的是根据高密度表面肌电图(HD-sEMG)网格与肌纤维的旋转来精确评估其错位。为此,本研究采用了一种通用的定量方法,即在随机和复杂信号的连通性和方向性研究中广泛应用的非线性相关系数(h2)。该方法通过(8×8)模拟电极矩阵应用于模拟数据,该电极矩阵使用多层圆柱形模型(肌肉、脂肪组织和皮肤组织)放置在模拟肱二头肌上。模拟5种不同的解剖结构和5种不同的运动单元招募模式,在从- 10°到10°的网格旋转中,在最大自愿收缩(MVC)的20%、40%和60%的三种恒定收缩水平下进行计算,根据肌肉纤维的一步为1度。得到的结果表明,非线性相关参数即图像矩阵值的平均值对网格旋转角度具有重要的敏感性。此外,该参数似乎对收缩程度和肌肉解剖结构也很敏感。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Precise assessment of HD-sEMG grid misalignment using nonlinear correlation
The aim of this paper is to perform a precise assessment of the misalignment of high-density surface electromyogram (HD-sEMG) grid according to its rotation with the muscle fibers. For this purpose, a generic and quantitative method was used in this study, namely the nonlinear correlation coefficient (h2) that is widely applied in connectivity and directionality of stochastic and complex signals. This approach is applied on simulated data through an (8×8) simulated electrode matrix placed on the simulated Biceps Brachii using a multilayered cylindrical model (muscle, fat tissue and skin tissue). Simulations with 5 different anatomies and 5 different motor unit recruitment patterns were computed at three constant contractions levels 20%, 40%, and 60% of the maximal voluntary contraction (MVC) for grid rotation from −10° to 10°, with a step of one degree according to muscle fibers. The obtained results show an important sensitivity of the nonlinear correlation parameter, namely the mean of the image matrix value, to the grid rotation angle. Furthermore, this parameter seems to be also sensitive to the contraction level and muscle anatomy.
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