A Personalized Trajectory Planning Approach for Exoskeleton Robots Using GPR and Fourier Series

To address the unique gait characteristics of individuals, this paper proposes a personalized trajectory planning method for exoskeleton robots. Gait trajectory data is collected using an inertial motion capture system, and personalized musculoskeletal models are built via OpenSim 4.5 to calculate joint angle data. The Trainable Time Warping (TTW) algorithm is used to align data from different time series, followed by Gaussian Mixture Model and Gaussian Mixture Regression (GMM-GMR) to fit multiple data sequences. The fitted joint angle curves are expanded using Fourier series to obtain Fourier coefficients. A Gaussian Process Regression (GPR) model is then established to map anthropometric parameters (thigh length, calf length, weight) to Fourier coefficients, which are used to plan gait trajectories. Experiments conducted with a lower limb exoskeleton robot demonstrate that this trajectory planning method achieves accurate trajectory tracking, with a root mean square error (RMSE) of 1.82° for the hip joint and 1.89° for the knee joint. The method also yielded a high user satisfaction rate of 90%, confirming its effectiveness in generating personalized and comfortable gait patterns.