Data-Mined Continuous Hip-Knee Coordination Mapping With Motion Lag for Lower-Limb Prosthesis Control

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.