title: Surrogate modeling of deformable joint contact using artificial neural networks.
abstract: Deformable joint contact models can be used to estimate loading conditions for cartilage-cartilage, implant-implant, human-orthotic, and foot-ground interactions. However, contact evaluations are often so expensive computationally that they can be prohibitive for simulations or optimizations requiring thousands or even millions of contact evaluations. To overcome this limitation, we developed a novel surrogate contact modeling method based on artificial neural networks (ANNs). The method uses special sampling techniques to gather input-output data points from an original (slow) contact model in multiple domains of input space, where each domain represents a different physical situation likely to be encountered. For each contact force and torque output by the original contact model, a multi-layer feed-forward ANN is defined, trained, and incorporated into a surrogate contact model. As an evaluation problem, we created an ANN-based surrogate contact model of an artificial tibiofemoral joint using over 75,000 evaluations of a fine-grid elastic foundation (EF) contact model. The surrogate contact model computed contact forces and torques about 1000 times faster than a less accurate coarse grid EF contact model. Furthermore, the surrogate contact model was seven times more accurate than the coarse grid EF contact model within the input domain of a walking motion. For larger input domains, the surrogate contact model showed the expected trend of increasing error with increasing domain size. In addition, the surrogate contact model was able to identify out-of-contact situations with high accuracy. Computational contact models created using our proposed ANN approach may remove an important computational bottleneck from musculoskeletal simulations or optimizations incorporating deformable joint contact models.
authors.affiliation: Department of Mechanical & Aerospace Engineering, University of Florida, Gainesville, FL, USA.
authors.forename: Ilan
authors.initials: I
authors.lastname: Eskinazi
authors.affiliation: Department of Mechanical & Aerospace Engineering, University of Florida, Gainesville, FL, USA; Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA; Department of Orthopaedics and Rehabilitation, University of Florida, Gainesville, FL, USA. Electronic address: fregly@ufl.edu.
authors.forename: Benjamin J
authors.initials: BJ
authors.lastname: Fregly
mesh_terms: D006801:Humans; D007719:Knee Joint / Q000503:physiopathology; D007720:Knee Prosthesis; D008954:Models, Biological; D009038:Motion; D016571:Neural Networks, Computer; D013997:Time Factors; D019415:Torque; D016138:Walking
keywords: Biomechanics; Elastic contact; Knee contact; Meta-model; Neural networks; Response surface; Surrogate modeling; Tibiofemoral joint