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J Biomech Eng. 2017 Aug 1;139(8). doi: 10.1115/1.4036605.

Evaluation of a Surrogate Contact Model in Force-Dependent Kinematic Simulations of Total Knee Replacement.

Author information

1
Orthopaedic Research Laboratory, Radboud Institute for Health Sciences, Radboud University Medical Center, P. O. Box 9101, Nijmegen 6500 HB, The Netherlands e-mail: Marco.Marra@radboudumc.nl.
2
Aalborg University, Department of Mechanical and Manufacturing Engineering, Fibigerstraede 16, Aalborg DK-9220, Denmark e-mail: msa@m-tech.aau.dk.
3
AnyBody Technology A/S, Niels Jernes Vej 10, Aalborg DK-9220, Denmark e-mail: md@anybodytech.com.
4
Department of Biomechanical Engineering, University of Twente, P. O. Box 217, Enschede 7500 AE, The Netherlands e-mail: h.f.j.m.koopman@utwente.nl.
5
Orthopaedic Research Laboratory, Radboud Institute for Health Sciences, Radboud University Medical Center, P. O. Box 9101, Nijmegen 6500 HB, The Netherlands e-mail: Dennis.Janssen@radboudumc.nl.
6
Orthopaedic Research Laboratory, Radboud Institute for Health Sciences, Radboud University Medical Center, P. O. Box 9101, Nijmegen 6500 HB, The Netherlands;Department of Biomechanical Engineering, University of Twente, P. O. Box 217, Enschede 7500 AE, The Netherlands e-mail: Nico.Verdonschot@radboudumc.nl.

Abstract

Knowing the forces in the human body is of great clinical interest and musculoskeletal (MS) 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 (FDK) 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 (ANN) learned the relationship between TF pose and loads from the medial and lateral sides of the TKR 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 N·m over all simulated activities. Secondary knee kinematics were predicted with RMSE lower than 0.2 mm and 0.2 deg. 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.

PMID:
28462424
DOI:
10.1115/1.4036605
[Indexed for MEDLINE]

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