Predicting Paretic Gait Trajectories Using sEMG with the ISSA-CNN-SVR Model

The continuous prediction of the desired gait trajectory of the paretic leg is a key task for the lower limb rehabilitation robot to assist the coordinated motion of the healthy and paretic legs of stroke patients, which has important research significance. In this paper, we propose a continuous prediction method for the desired gait trajectory of the paretic leg based on the sEMG signal of the healthy leg. In this method, an ISSA-CNN-SVR cascade network prediction model is designed. Based on this model, the mapping relationship between the sEMG signal of the healthy leg and the hip and knee joint angle of the paretic leg is established. The model input is the 8-channel sEMG signal of the healthy leg in the current instantaneous moment, and the output is the desired gait trajectory (hip and knee joint angle trajectories) of the paretic leg in the instantaneous moment after 100 ms. We recruited eight healthy participants for the validation experiments. Results indicate that the proposed method can continuously and smoothly predict the hip and knee joint angle trajectories of the paretic leg for the instantaneous moment after 100 ms based on the sEMG signal of the healthy leg and the ISSA-CNN-SVR model. The average RMSE of the predicted hip and knee joint angle trajectories of the paretic leg are 4.643° and 6.845°, with average R ² being 0.855 and 0.857, respectively, demonstrating a high level of fitting. Therefore, this study is of great significance to improve the gait coordination of the healthy and paretic legs of robot-assisted stroke patients in the process of rehabilitation training.