To generate individualized reference gait pattern for passive rehabilitation training more accurately

a gait pattern prediction model combining random forest(RF)and probabilistic movement primitives(ProMPs)is proposed.Firstly

RF is used to predict the gait characteristics of individuals based on their physical characteristic parameters and specified gait speed.Then

ProMPs are introduced to learn the probability distribution of normal human gait data and reconstruct gait patterns based on the predicted gait characteristics.The experimental results show that compared with the existing methods

the mean absolute deviation(MAD)of the hip and knee gait patterns predicted by the RF-ProMPs model are reduced by 32% and 18%

respectively.The proposed method can be used to generate reference trajectories for lower limb passive rehabilitation training.