IEEE Trans Neural Syst Rehabil Eng. 2019 Sep;27(9):1920-1930. doi: 10.1109/TNSRE.2019.2933811. Epub 2019 Aug 8.
This study proposes a novel preprocessing method integrating muscle activation heterogeneity analysis and kurtosis-guided filtering to realize high-accuracy surface electromyogr-aphy (sEMG)-based force estimation. A total of 10 subjects were recruited. Each subject performed isometric elbow flexion tasks at 20%, 40%, and 60% maximum voluntary contraction (MVC) target force levels, and the joint force and high-density sEMG (HD-sEMG) signals from biceps brachii and brachialis were collected synchronously. The force estimation model was built using three-order polynomial fitting technique. The input signal extraction of the force model, also named as the preprocessing of HD-sEMG signal, was carried out in the following procedures: first, HD-sEMG signals were decomposed by principal component analysis into principal components and weight vectors; second, the first several weight maps were segmented to obtain heterogeneity information by the Otsu and Moore-Neighbor tracing methods, and the principal component covering the most activated areas was selected; and last, a kurtosis-guided filter was designed to process the selected principal component to obtain the input signal. For the sake of comparison, the joint force estimation experiments based ON five preprocessing methods were conducted. The experimental results demonstrated that the proposed method obtained 52%, 53%, and 59% reduction in the mean root mean square difference at 20% MVC, 40% MVC, and 60% MVC force-level tasks, respectively, compared to the preprocessing method with the first principal component plus fixed parameter filtering. This proposed HD-sEMG pre-processing method has reliable neuromuscular electro-physiological foundation, and has good application value for realizing high-accuracy muscle/joint force estimation in the fields of rehabilitation engineering, sports biomechanics, and muscle disease diagnosis etc.
本研究提出了一种新颖的预处理方法，该方法集成了肌肉激活异质性分析和峰度引导滤波，以实现基于表面肌电（sEMG）的高精度力估计。共招募了 10 名受试者。每位受试者以 20%、40%和 60%最大自主收缩（MVC）目标力水平进行等长肘屈伸任务，同步采集肱二头肌和肱肌的关节力和高密度 sEMG（HD-sEMG）信号。力估计模型采用三阶多项式拟合技术构建。力模型的输入信号提取，也称为 HD-sEMG 信号的预处理，按以下步骤进行：首先，HD-sEMG 信号通过主成分分析分解为主成分和权向量；其次，通过 Otsu 和 Moore-Neighbor 跟踪方法对前几个权值图进行分割，获得异质性信息，并选择覆盖最活跃区域的主成分；最后，设计峰度引导滤波器对所选主成分进行处理，以获得输入信号。为了进行比较，基于五种预处理方法进行了关节力估计实验。实验结果表明，与基于第一主成分加固定参数滤波的预处理方法相比，该方法在 20% MVC、40% MVC 和 60% MVC 力水平任务中，分别将平均均方根差降低了 52%、53%和 59%。提出的 HD-sEMG 预处理方法具有可靠的神经肌肉电生理基础，在康复工程、运动生物力学和肌肉疾病诊断等领域实现高精度肌肉/关节力估计具有很好的应用价值。
