Predicting foot orthosis deformation based on its contour kinematics during walking

Background: Customized foot orthoses (FOs) are designed based on foot posture and function, while the interaction between these metrics and FO deformation remains unknown due to technical problems. Our aim was to predict FO deformation under dynamic loading using an artificial intelligence (AI) approach, and to report the deformation of two FOs of different stiffness during walking. Methods: Each FO was fixed on a plate, and six triad reflective markers were fitted on its contour, and 55 markers on its plantar surface. Manual loadings with known magnitude and application point were applied to deform “sport” and “regular” (stiffer) FOs in all regions (training session). Then, 13 healthy male subjects walked with the same FOs inside shoes, where the triad markers were visible by means of shoe holes (walking session). The marker trajectories were recorded using optoelectronic system. A neural network was trained to find the dependency between the orientation of triads on FO contour and the position of markers on its plantar surface. After tuning hyperparameters and evaluating the performance of the model, marker positions on FOs surfaces were predicted during walking for each subject. Statistical parametric mapping was used to compare the pattern of deformation between two FOs. Results: Overall, the model showed an average error of