Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China.
Sensors (Basel). 2018 Jul 11;18(7):2238. doi: 10.3390/s18072238.
A novel framework based on the fast orthogonal search (FOS) method coupled with factorization algorithms was proposed and implemented to realize high-accuracy muscle force estimation via surface electromyogram (SEMG). During static isometric elbow flexion, high-density SEMG (HD-SEMG) signals were recorded from upper arm muscles, and the generated elbow force was measured at the wrist. HD-SEMG signals were decomposed into time-invariant activation patterns and time-varying activation curves using three typical factorization algorithms including principal component analysis (PCA), independent component analysis (ICA), and nonnegative matrix factorization (NMF). The activation signal of the target muscle was obtained by summing the activation curves, and the FOS algorithm was used to create basis functions with activation signals and establish the force estimation model. Static isometric elbow flexion experiments at three target levels were performed on seven male subjects, and the force estimation performances were compared among three typical factorization algorithms as well as a conventional method for extracting the average signal envelope of all HD-SEMG channels (AVG-ENVLP method). The overall root mean square difference (RMSD) values between the measured forces and the estimated forces obtained by different methods were 11.79 ± 4.29% for AVG-ENVLP, 9.74 ± 3.77% for PCA, 9.59 ± 3.81% for ICA, and 9.51 ± 4.82% for NMF. The results demonstrated that, compared to the conventional AVG-ENVLP method, factorization algorithms could substantially improve the performance of force estimation. The FOS method coupled with factorization algorithms provides an effective way to estimate the combined force of multiple muscles and has potential value in the fields of sports biomechanics, gait analysis, prosthesis control strategy, and exoskeleton devices for assisted rehabilitation.
提出并实现了一种基于快速正交搜索（FOS）方法与分解算法相结合的新框架，通过表面肌电（SEMG）实现高精度肌肉力估计。在静态等长肘屈时，从上臂肌肉记录高密度 SEMG（HD-SEMG）信号，并在手腕处测量产生的肘部力。使用三种典型的分解算法，包括主成分分析（PCA）、独立成分分析（ICA）和非负矩阵分解（NMF），将 HD-SEMG 信号分解为时不变激活模式和时变激活曲线。通过将激活曲线相加获得目标肌肉的激活信号，然后使用 FOS 算法创建具有激活信号的基函数并建立力估计模型。在 7 名男性受试者上进行了三个目标水平的静态等长肘屈实验，比较了三种典型分解算法以及一种提取所有 HD-SEMG 通道平均信号包络（AVG-ENVLP 方法）的常规方法的力估计性能。不同方法得到的测量力和估计力之间的总体均方根差（RMSD）值分别为 AVG-ENVLP 为 11.79 ± 4.29%，PCA 为 9.74 ± 3.77%，ICA 为 9.59 ± 3.81%，NMF 为 9.51 ± 4.82%。结果表明，与传统的 AVG-ENVLP 方法相比，分解算法可以显著提高力估计的性能。FOS 方法与分解算法相结合为估计多个肌肉的合力提供了一种有效方法，在运动生物力学、步态分析、假肢控制策略和辅助康复外骨骼设备等领域具有潜在的应用价值。
