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research-article

Evaluation of a surrogate contact model in multi-body dynamic simulations of Total Knee Replacement

[+] Author and Article Information
Marco A. Marra

Radboud University Medical Center, Radboud Institute for Health Sciences, Orthopaedic Research Laboratory P.O.Box 9101, 6500 HB Nijmegen, The Netherlands
Marco.Marra@radboudumc.nl

Michael S. Andersen

Aalborg University, Department of Mechanical and Manufacturing Engineering Fibigerstraede 16, DK-9220 Aalborg, Denmark
msa@m-tech.aau.dk

Michael Damsgaard

AnyBody Technology A/S Niels Jernes Vej 10, DK-9220 Aalborg, Denmark
md@anybodytech.com

Bart F.J.M. Koopman

University of Twente, Department of Biomechanical Engineering P.O.Box 217, 7500 AE Enschede, The Netherlands
h.f.j.m.koopman@utwente.nl

Dennis Janssen

Radboud University Medical Center, Radboud Institute for Health Sciences, Orthopaedic Research Laboratory P.O.Box 9101, 6500 HB Nijmegen, The Netherlands
dennis.janssen@radboudumc.nl

Nico Verdonschot

Radboud University Medical Center, Radboud Institute for Health Sciences, Orthopaedic Research Laboratory P.O.Box 9101, 6500 HB Nijmegen, The NetherlandsUniversity of Twente, Department of Biomechanical Engineering P.O.Box 217, 7500 AE Enschede, The Netherlands
Nico.Verdonschot@radboudumc.nl

1Corresponding author.

ASME doi:10.1115/1.4036605 History: Received June 24, 2016; Revised April 24, 2017

Abstract

Knowing the forces in the human body is of great clinical interest and musculoskeletal models are the most commonly used tool to estimate them in vivo. Unfortunately, the process of computing muscle, joint contact and ligament forces simultaneously is computationally highly demanding. The goal of this study was to develop a fast surrogate model of the tibiofemoral (TF) contact in a total knee replacement (TKR) model and apply it to force-dependent kinematic simulations of activities of daily living (ADLs). Multiple domains were populated with sample points from the reference TKR contact model, based on reference simulations and design-of-experiments. Artificial neural networks learned the relationship between TF pose and loads from the medial and lateral sides of the implant. Normal and right-turn gait, rising-from-a-chair, and a squat were simulated using both surrogate and reference contact models. Compared to the reference contact model, the surrogate contact model predicted TF forces with a root-mean-square error (RMSE) lower than 10 N and TF moments lower than 0.3 Nm over all simulated activities. Secondary knee kinematics were predicted with RMSE lower than 0.2 mm and 0.2 degrees. Simulations that used the surrogate contact model ran on average three times faster than those using the reference model, allowing the simulation of a full gait cycle in 4.5 min. This modeling approach proved fast and accurate enough to perform extensive parametric analyses, such as simulating subject-specific variations and surgical-related factors in TKR.

Copyright (c) 2017 by ASME
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