Personalized lower limb gait reconstruction modeling based on RFA-ProMP

Abstract To generate personalized gait reference trajectories for lower limb rehabilitation exoskeletons tailored to patients with different body types, we propose a combination of a random forest algorithm (RFA) with an attention mechanism and a mimicry learning method called Probabilistic Movement Primitives (ProMP). This approach aims to establish a personalized lower limb gait reconstruction model. Firstly, the RFA is utilized to map the individual body characteristic parameters and the relationship between gait speed and gait. It predicts the gait characteristics under a specified gait speed. Then, the ProMP is introduced to learn the probability distribution of normal gait data and perform the lower limb gait reconstruction based on the predicted gait characteristics. Experimental results demonstrate that the hip and knee curves reconstructed by RFA-ProMP exhibit the smallest error. Furthermore, the generated reference trajectory aligns with the original probabilistic reference trajectory. This method proves effective in generating personalized gait curves for the lower limbs.