Due to the strong dynamic and time-varying characteristics of the surface electromyography (sEMG) signal and the coupling between lower limb muscles, it is challenging to accurately estimate the joint angle of complex lower limb motion. Firstly, we proposed a wavelet packet decomposition correlation dimension (WPD_CD) analysis method. A three-level wavelet packet decomposes the sEMG signal, and the optimal and stable feature vector is extracted by the correlation dimension analysis. A singular spectrum nonlinear autoregressive exogenous model (NARX_SSA) is proposed to map the optimal eigenvector to the lower limb joint angle to improve the angle accuracy and smoothness. The experimental results show that this method only needs to collect three lower limb muscles and accurately estimate four movements of knee and hip angles. The overall RMSE was 0.32, the minimum RMSE was 0.20, and the standard deviation (SD) was 20.23. Compared with other methods, the accuracy of angle estimation was improved by six times, and the smoothness was improved by 13%. Therefore, this method can be effectively applied to the angle estimation of complex lower limb motion patterns.

中文翻译：

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一种基于sEMG信号的下肢关节角度估计新方法

由于表面肌电（sEMG）信号的强动态和时变特性以及下肢肌肉之间的耦合，准确估计复杂下肢运动的关节角度具有挑战性。首先，我们提出了一种小波包分解相关维数（WPD_CD）分析方法。一个三级小波包对sEMG信号进行分解，通过相关维分析提取最优稳定的特征向量。提出了奇异谱非线性自回归外生模型（NARX_SSA）将最优特征向量映射到下肢关节角度，以提高角度精度和平滑度。实验结果表明，该方法只需要采集三块下肢肌肉，就能准确估计出四次膝、髋角度的运动。总体 RMSE 为 0。32，最小RMSE为0.20，标准偏差（SD）为20.23。与其他方法相比，角度估计精度提高了6倍，平滑度提高了13%。因此，该方法可以有效地应用于复杂下肢运动模式的角度估计。