GSAHermNet: A GraphSAGE-Based Neural Network with Hermite Interpolation for Individualized Gait Pattern Generation

Accurate generation of gait patterns is essential for advancing robotic gait rehabilitation. This study presents GSAHermNet, a novel two-stage framework that combines a GraphSAGE-based neural network for predicting key gait events with Hermite interpolation to reconstruct full joint trajectories. Unlike conventional methods that generate the entire gait cycle directly, GSAHermNet focuses on predicting key gait events using only seven body and walking parameters, thereby reducing over fitting and enhancing generalizability across diverse walking speeds and conditions. The model was trained on a public dataset of 42 healthy subjects using 5-fold cross-validation on 40 individuals, while the remaining two subjects were reserved for independent testing. Experimental results demonstrate that GSAHermNet achieves mean absolute deviations (MAD) below 4.58° and correlation coefficients (r) of 0.99 for hip and knee joints, and MAD below 3.69° with r = 0.85 for the ankle. Comparative analyses confirm that GSAHermNet outperforms conventional statistical and machine learning approaches in both accuracy and robust ness. The proposed approach has great potential for real word applications, such as adaptive control in functional electrical stimulation systems and personalized motion planning in lower-limb exoskeletons. An online framework for real-time gait trajectory generation will be established using wearable sensor inputs in future.