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Research Papers

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

[+] Author and Article Information
Marco A. Marra

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

Michael S. Andersen

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

Michael Damsgaard

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

Bart F. J. M. Koopman

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

Dennis Janssen

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

Nico Verdonschot

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

1Corresponding author.

Manuscript received June 24, 2016; final manuscript received April 24, 2017; published online June 7, 2017. Assoc. Editor: Paul Rullkoetter.

J Biomech Eng 139(8), 081001 (Jun 07, 2017) (10 pages) Paper No: BIO-16-1267; doi: 10.1115/1.4036605 History: Received June 24, 2016; Revised April 24, 2017

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.

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Figures

Grahic Jump Location
Fig. 1

Contact model of TKR. The TF pose is defined by the relative translation and rotation between the femoral component frame (upper) and the tibial component frame (lower). Tibiofemoral translations are expressed in the tibial component frame of reference, and represent anterior femur translation (x), joint distraction (y), and medial femur translation (z). Tibiofemoral rotations are expressed in the femoral component frame of reference with Cardan angles using the z–y–x sequence of rotations, where the first rotation represents knee extension, the second, tibial external rotation, and the third, knee abduction. Rigid surface contact based on pressure-overclosure is defined between medial and lateral side of tibial component and femoral component. To obtain samples for the surrogate model, this contact model is evaluated using repeated static analyses.

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Fig. 2

Diagram of the two-stage feedforward ANN. The stage I network (left) learned the relations between TF loads in sensitive directions (FyMed, TxMed, FyLat, TxLat) and the TF pose parameters; in stage II (right) the remaining TF loads of medial (lateral) side are obtained as functions of the TF pose and the medial (lateral) TF loads from stage I. HL, hidden layers; W, network weights; b, network biases.

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Fig. 3

Medial (top) and lateral (bottom) TF compressive forces during normal gait, right-turn, rising-from-a-chair, and squat simulation. Reference-measured force (eTibia, shaded), predictions using surrogate (solid) and reference (dotted) contact model.

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Fig. 4

Anterior tibial translation, joint distraction, lateral tibial translation, knee flexion, knee adduction, and tibial external rotation predicted using the reference contact model (solid line) and the surrogate contact model (dotted line) during normal gait, right-turn, rising-from-a-chair, and squat simulation

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