Estimating a Complete Gait Cycle of Ground Reaction Data Using Only Two Force Plates for Predictive Simulations of Walking

Personalized predictive simulations of walking generated using direct collocation optimal control have potential for informing the design of treatments for movement disorders. Ideally, a tracking optimization that closely reproduces experimental walking data collected from the patient would serve as the starting point of the computational treatment design process. Tracking optimizations require a full cycle of walking data (heel strike-to-heel strike of the same foot), but motion labs with only two overground force plates have missing ground reaction data at the start of the gait cycle. This study presents a novel method for estimating these missing ground reaction data. The estimated data are validated using a foot-ground contact (FGC) model calibrated using the Neuromusculoskeletal Modeling (NMSM) Pipeline. The calibrated FGC model could accurately reproduce the estimated ground reactions with the original foot kinematics. These results imply that estimated ground reactions can be used for a tracking optimization to simulate walking motion.