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.

References

1.
Erdemir
,
A.
,
McLean
,
S.
,
Herzog
,
W.
, and
van den Bogert
,
A. J.
,
2007
, “
Model-Based Estimation of Muscle Forces Exerted During Movements.
,”
Clin. Biomech. (Bristol, Avon)
,
22
(
2
), pp.
131
154
.
2.
Andersen
,
M. S.
,
Damsgaard
,
M.
, and
Rasmussen
,
J.
,
2011
, “
Force-Dependent Kinematics: A New Analysis Method for Non-Conforming Joints
,”
13th International Symposium on Computer Simulation in Biomechanics
(
TGCS
), Leuven, Belgium, June 30–July 2.
3.
Marra
,
M. A.
,
Vanheule
,
V.
,
Fluit
,
R.
,
Koopman
,
B. H. F. J. M.
,
Rasmussen
,
J.
,
Verdonschot
,
N.
, and
Andersen
,
M. S.
,
2015
, “
A Subject-Specific Musculoskeletal Modeling Framework to Predict In Vivo Mechanics of Total Knee Arthroplasty
,”
ASME J. Biomech. Eng.
,
137
(
2
), p.
20904
.
4.
Halloran
,
J. P.
,
Erdemir
,
A.
, and
van den Bogert
,
A. J.
,
2009
, “
Adaptive Surrogate Modeling for Efficient Coupling of Musculoskeletal Control and Tissue Deformation Models
,”
ASME J. Biomech. Eng.
,
131
(
1
), p.
11014
.
5.
Mishra
,
M.
,
Derakhshani
,
R.
,
Paiva
,
G. C.
, and
Guess
,
T. M.
,
2011
, “
Nonlinear Surrogate Modeling of Tibio-Femoral Joint Interactions
,”
Biomed. Signal Process. Control
,
6
(
2
), pp.
164
174
.
6.
Lin
,
Y.-C.
,
Farr
,
J.
,
Carter
,
K.
, and
Fregly
,
B. J.
,
2006
, “
Response Surface Optimization for Joint Contact Model Evaluation
,”
J. Appl. Biomech.
,
22
(
2
), pp.
120
130
.
7.
Lu
,
Y.
,
Pulasani
,
P. R.
,
Derakhshani
,
R.
, and
Guess
,
T. M.
,
2013
, “
Application of Neural Networks for the Prediction of Cartilage Stress in a Musculoskeletal System
,”
Biomed. Signal Process. Control
,
8
(
6
), pp.
475
482
.
8.
Lin
,
Y.-C.
,
Haftka
,
R. T.
,
Queipo
,
N. V.
, and
Fregly
,
B. J.
,
2010
, “
Surrogate Articular Contact Models for Computationally Efficient Multibody Dynamic Simulations
,”
Med. Eng. Phys.
,
32
(
6
), pp.
584
594
.
9.
Lin
,
Y.-C.
,
Walter
,
J. P.
,
Banks
,
S. A.
,
Pandy
,
M. G.
, and
Fregly
,
B. J.
,
2010
, “
Simultaneous Prediction of Muscle and Contact Forces in the Knee During Gait
,”
J. Biomech.
,
43
(
5
), pp.
945
952
.
10.
Lin
,
Y.-C.
,
Haftka
,
R. T.
,
Queipo
,
N. V.
, and
Fregly
,
B. J.
,
2009
, “
Two-Dimensional Surrogate Contact Modeling for Computationally Efficient Dynamic Simulation of Total Knee Replacements
,”
ASME J. Biomech. Eng.
,
131
(
4
), p.
41010
.
11.
Eskinazi
,
I.
, and
Fregly
,
B. J.
,
2015
, “
Surrogate Modeling of Deformable Joint Contact Using Artificial Neural Networks
,”
Med. Eng. Phys.
,
37
(
9
), pp.
885
891
.
12.
Hornik
,
K.
,
Stinchcombe
,
M.
, and
White
,
H.
,
1989
, “
Multilayer Feedforward Networks are Universal Approximators
,”
Neural Networks
,
2
(
5
), pp.
359
366
.
13.
Damsgaard
,
M.
,
Rasmussen
,
J.
,
Christensen
,
S. T.
,
Surma
,
E.
, and
de Zee
,
M.
,
2006
, “
Analysis of Musculoskeletal Systems in the AnyBody Modeling System
,”
Simul. Model. Pract. Theory
,
14
(
8
), pp.
1100
1111
.
14.
Andersen
,
M. S.
,
Damsgaard
,
M.
, and
Rasmussen
,
J.
,
2009
, “
Kinematic Analysis of Over-Determinate Biomechanical Systems
,”
Comput. Methods Biomech. Biomed. Eng.
,
12
(
4
), pp.
371
384
.
15.
Fregly
,
B. J.
,
Besier
,
T. F.
,
Lloyd
,
D. G.
,
Delp
,
S. L.
,
Banks
,
S. A.
,
Pandy
,
M. G.
, and
D'Lima
,
D. D.
,
2012
, “
Grand Challenge Competition to Predict In Vivo Knee Loads
,”
J. Orthop. Res.
,
30
(
4
), pp.
503
513
.
16.
Hammersley
,
J. M.
,
2006
, “
Monte Carlo Methods for Solving Multivariable Problems
,”
Ann. N. Y. Acad. Sci.
,
86
(
3
), pp.
844
874
.
17.
Guennebaud
,
G.
, and Jacob, B.,
2010
, “
Eigen v3
,” Eigen Software Ltd, Birmingham, UK, accessed July 18, 2016, http://eigen.tuxfamily.org
18.
Grood
,
E. S.
, and
Suntay
,
W. J.
,
1983
, “
A Joint Coordinate System for the Clinical Description of Three-Dimensional Motions: Application to the Knee
,”
ASME J. Biomech. Eng.
,
105
(
2
), pp.
136
144
.
19.
D'Lima
,
D. D.
,
Fregly
,
B. J.
,
Patil
,
S.
,
Steklov
,
N.
, and
Colwell
,
C. W.
,
2012
, “
Knee Join Forces: Prediction, Measurement, and Significance
,”
Proc. Inst. Mech. Eng. H
,
226
(
2
), pp.
95
102
.
20.
Bei
,
Y.
, and
Fregly
,
B. J.
,
2004
, “
Multibody Dynamic Simulation of Knee Contact Mechanics
,”
Med. Eng. Phys.
,
26
(
9
), pp.
777
789
.
21.
Fregly
,
B. J.
,
Sawyer
,
W. G.
,
Harman
,
M. K.
, and
Banks
,
S. A.
,
2005
, “
Computational Wear Prediction of a Total Knee Replacement From In Vivo Kinematics
,”
J. Biomech.
,
38
(
2
), pp.
305
314
.
22.
Fregly
,
B. J.
,
Banks
,
S. A.
,
D'Lima
,
D. D.
, and
Colwell
,
C. W.
,
2008
, “
Sensitivity of Knee Replacement Contact Calculations to Kinematic Measurement Errors
,”
J. Orthop. Res.
,
26
(
9
), pp.
1173
1179
.
23.
Eskinazi
,
I.
, and
Fregly
,
B. J.
,
2016
, “
An Open-Source Toolbox for Surrogate Modeling of Joint Contact Mechanics
,”
IEEE Trans. Biomed. Eng.
,
63
(
2
), pp.
269
277
.
24.
Fregly
,
B. J.
,
Bei
,
Y.
, and
Sylvester
,
M. E.
,
2003
, “
Experimental Evaluation of an Elastic Foundation Model to Predict Contact Pressures in Knee Replacements
,”
J. Biomech.
,
36
(
11
), pp.
1659
1668
.
You do not currently have access to this content.