Department of Electronic Science and Technology, University of Science and Technology of China (USTC), Hefei, People's Republic of China.
J Neural Eng. 2017 Aug;14(4):046005. doi: 10.1088/1741-2552/aa63ba.
To realize accurate muscle force estimation, a novel framework is proposed in this paper which can extract the input of the prediction model from the appropriate activation area of the skeletal muscle.
Surface electromyographic (sEMG) signals from the biceps brachii muscle during isometric elbow flexion were collected with a high-density (HD) electrode grid (128 channels) and the external force at three contraction levels was measured at the wrist synchronously. The sEMG envelope matrix was factorized into a matrix of basis vectors with each column representing an activation pattern and a matrix of time-varying coefficients by a nonnegative matrix factorization (NMF) algorithm. The activation pattern with the highest activation intensity, which was defined as the sum of the absolute values of the time-varying coefficient curve, was considered as the major activation pattern, and its channels with high weighting factors were selected to extract the input activation signal of a force estimation model based on the polynomial fitting technique.
Compared with conventional methods using the whole channels of the grid, the proposed method could significantly improve the quality of force estimation and reduce the electrode number.
The proposed method provides a way to find proper electrode placement for force estimation, which can be further employed in muscle heterogeneity analysis, myoelectric prostheses and the control of exoskeleton devices.
为了实现精确的肌肉力估计，本文提出了一种新的框架，该框架可以从骨骼肌的适当激活区域中提取预测模型的输入。
使用高密度（HD）电极网格（128 个通道）采集等长肘屈伸过程中肱二头肌的表面肌电（sEMG）信号，并在腕关节处同步测量三个收缩水平的外力。通过非负矩阵分解（NMF）算法，将 sEMG 包络矩阵分解为基向量矩阵，其中每列代表一种激活模式，以及时变系数矩阵。将激活强度最高的激活模式定义为时变系数曲线的绝对值之和，并将其通道的高加权因子用于提取基于多项式拟合技术的力估计模型的输入激活信号。
与使用网格全部通道的传统方法相比，所提出的方法可以显著提高力估计的质量并减少电极数量。
该方法为力估计的适当电极放置提供了一种方法，可进一步应用于肌肉异质性分析、肌电假肢和外骨骼设备的控制。
