Application of physics-informed neural networks for ground reaction force estimation in human gait

Ground reaction force (GRF) estimation during gait is essential for analyzing human locomotion. Though several conventional machine learning models have been proposed for GRF estimation, their dependence on data often limits generalizability and prediction accuracy. To address these limitations, the present study explores the feasibility of Physics-Informed Neural Networks (PINNs) for GRF estimation. The developed PINN model was evaluated visually using plots and quantitatively using Root Mean Square Error (RMSE). Results indicate that applying PINN led to a significant RMSE reduction across all subjects, with improvements ranging from 67% to 89%, thereby highlighting the effectiveness of the proposed approach.