IEEE Trans Neural Syst Rehabil Eng. 2022;30:1557-1566. doi: 10.1109/TNSRE.2022.3179978. Epub 2022 Jun 14.
Trajectory planning of the knee joint plays an essential role in controlling the lower limb prosthesis. Nowadays, the idea of mapping the trajectory of the healthy limb to the motion trajectory of the prosthetic joint has begun to emerge. However, establishing a simple and intuitive coordination mapping is still challenging. This paper employs the method of experimental data mining to explore such a coordination mapping. The coordination indexes, i.e., the mean absolute relative phase (MARP) and the deviation phase (DP), are obtained from experimental data. Statistical results covering different subjects indicate that the hip motion possesses a stable phase difference with the knee, inspiring us to construct a hip-knee Motion-Lagged Coordination Mapping (MLCM). The MLCM first introduces a time lag to the hip motion to avoid conventional integral or differential calculations. The model in polynomials, which is proved more efficient than Gaussian process regression and neural network learning, is then constructed to represent the mapping from the lagged hip motion to the knee motion. In addition, a strong linear correlation between hip-knee MARP and hip-knee motion lag is discovered for the first time. By using the MLCM, one can generate the knee trajectory for the prosthesis control only via the hip motion of the healthy limb, indicating less sensing and better robustness. Numerical simulations show that the prosthesis can achieve normal gaits at different walking speeds.
膝关节轨迹规划在控制下肢假肢中起着至关重要的作用。如今，将健康肢体的轨迹映射到假肢关节的运动轨迹的想法已经开始出现。然而，建立一个简单直观的协调映射仍然具有挑战性。本文采用实验数据挖掘的方法来探索这种协调映射。协调指标，即平均绝对相对相位（MARP）和偏差相位（DP），是从实验数据中获得的。涵盖不同受试者的统计结果表明，髋关节运动与膝关节具有稳定的相位差，这启发我们构建一个髋膝运动滞后协调映射（MLCM）。MLCM 首先为髋关节运动引入一个时滞，以避免传统的积分或微分计算。然后构建多项式模型来表示从滞后髋关节运动到膝关节运动的映射，该模型被证明比高斯过程回归和神经网络学习更有效。此外，首次发现髋关节 MARP 和髋关节运动滞后之间存在很强的线性相关性。通过使用 MLCM，仅通过健康肢体的髋关节运动就可以为假肢控制生成膝关节轨迹，这表明感知更少，鲁棒性更好。数值模拟表明，假肢可以在不同的行走速度下实现正常步态。
