Open AccessA Reduced-Order Computational Model of a Semi-Active Variable-Stiffness Foot Prosthesis
Author(s) -
Michael A. McGeehan,
Peter G. Adamczyk,
Kieran M. Nichols,
Michael E. Hahn
Publication year - 2021
Publication title -
journal of biomechanical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.546
H-Index - 126
eISSN - 1528-8951
pISSN - 0148-0731
DOI - 10.1115/1.4050456
Subject(s) - stiffness , mean squared error , gait , prosthesis , ground reaction force , materials science , simulation , structural engineering , biomedical engineering , mathematics , computer science , engineering , physics , kinematics , statistics , physiology , classical mechanics , artificial intelligence , biology
Passive energy storage and return (ESR) feet are current performance standard in lower limb prostheses. A recently developed semi-active variable-stiffness foot (VSF) prosthesis balances the simplicity of a passive ESR device with the adaptability of a powered design. The purpose of this study was to model and simulate the ESR properties of the VSF prosthesis. The ESR properties of the VSF were modeled as a lumped parameter overhung beam. The overhung length is variable, allowing the model to exhibit variable ESR stiffness. Foot-ground contact was modeled using sphere-to-plane contact models. Contact parameters were optimized to represent the geometry and dynamics of the VSF and its foam base. Static compression tests and gait were simulated. Simulation outcomes were compared to corresponding experimental data. Stiffness of the model matched that of the physical VSF (R2: 0.98, root-mean-squared error (RMSE): 1.37 N/mm). Model-predicted resultant ground reaction force (GRFR) matched well under optimized parameter conditions (R2: 0.98, RMSE: 5.3% body weight,) and unoptimized parameter conditions (R2: 0.90, mean RMSE: 13% body weight). Anterior-posterior center of pressure matched well with R2 > 0.94 and RMSE < 9.5% foot length in all conditions. The ESR properties of the VSF were accurately simulated under benchtop testing and dynamic gait conditions. These methods may be useful for predicting GRFR arising from gait with novel prostheses. Such data are useful to optimize prosthesis design parameters on a user-specific basis.