Bernardo Innocenti

Friedl De Groote

Ilse Jonkers

Lennart Scheys

Luc Labey

Pierre Gillen

Silvia Pianigiani

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Copyright © 2013 by ASME 1 
INTRODUCTION 
In this study, we present an innovative methodology (Figure 1) to 
calculate patient specific tibio-femoral (TF) contact forces by 
integrating medical image data, 3D skin-mounted marker trajectories, 
ground reaction forces, electromyography (EMG) data and finite 
element analysis (FEA). The muscle redundancy problem is solved 
through an EMG-constrained optimization approach. Calculated 
muscle forces are input to a FEA to calculate TF contact forces. 
Kinematics of the degrees of freedom (DOFs) of the knee that cannot 
be accurately assessed from the trajectories of skin-mounted markers, 
are estimated using a novel iterative procedure which combines 
muscle force calculation with dynamic FEA. The presented 
methodology is applied to analyze TF contact forces of a walking trial 
performed on an instrumented treadmill of which the speed was 
sequentially ramped up and down. The results presented in this 
abstract will be validated against the in-vivo measured TF contact 
forces. 
 
METHODS 
 Based on the marker trajectories, ground reaction forces, EMG 
signals, implant geometry and medical image data provided by the 
ASME Grand Challenge organizers [1], medial and lateral TF contact 
forces were calculated for subject JW (height 168cm, weight 66.7kg) 
during one gait cycle of the accelerating phase (gc1) and one gait cycle 
of the decelerating phase (gc2) of a walking trial on an instrumented 
treadmill.  
 Figure 1 outlines the innovative methodology that has been 
applied for calculating TF contact forces. All multi-body simulations 
were based on the provided OpenSim [2] model in which the TF joint 
has six DOFs. Musculotendon (MT) parameters were taken from the 
model of Delp [3] and scaled with respect to the MT lengths in the 
anatomic position. 
 During inverse kinematics (step1), the patella-femoral flexion 
was constrained to be two thirds of the knee flexion [4] while the other 
translations and rotations describing patella motion with respect to the 
femur were kept constant. Kinematics, of all remaining DOFs in the 
model, was estimated using a Kalman smoother algorithm [5] 
implemented as an OpenSim tool. 
 
Figure 1: Schematic outline of the implemented methodology. 
 
 Joint reaction moments were calculated using an inverse dynamic 
analysis (Opensim, step 2) and were input to two consecutive static 
optimization procedures to calculate individual muscle activations 
(step3):  
1.  First, muscle activations were constrained to lie between 0 and 1, 
i.e. the physiological limits.  
2. Second, the muscle activations were constrained below based on 
the experimental surface EMG. The EMG was rectified and 
scaled to the maximal muscle activations calculated by the first 
 
TIBIO-FEMORAL CONTACT FORCE DURING GAIT: AN ITERATIVE METHOD USING 
EMG-CONSTRAINED MULTI-BODY SIMULATION AND FINITE ELEMENT ANALYSIS 
Silvia Pianigiani
1
, Friedl De Groote
2
, Lennart Scheys
3
, Pierre Gillen
4
, Luc Labey
3
, Bernardo 
Innocenti
5
 and Ilse Jonkers
4
 
1
 IRCCS, Istituto Ortopedico Galeazzi, Milano, Italy 
2
Department of Mechanical Engineering, KU Leuven, Leuven, Belgium 
3
European Centre for Knee Research, Smith&Nephew, Leuven, Belgium 
4
Department of Biomedical Kinesiology, KU Leuven, Leuven, Belgium 
5
BEAMS Department, Université Libre de Bruxelles, Bruxelles, Belgium 
 
Proceedings of the ASME 2013 Summer Bioengineering Conference 
SBC2013 
June 26-29, 2013, Sunriver, Oregon, USA 
SBC2013-14494
Downloaded From: https://proceedings.asmedigitalcollection.asme.org on 07/02/2019 Terms of Use: http://www.asme.org/about-asme/terms-of-use
Copyright © 2013 by ASME 2 
static optimization (1). The lower bound on the muscle activation 
was set to half of the scaled EMG signal. Since EMG was only 
available for 14 superficial muscles, muscles were grouped with 
relevant synergists. For some muscles, no relevant synergist 
could be identified and muscle activations were unconstrained.  
In step 4 of the procedure, a FEA model was built based on the 
available CT image data and the implant geometry. Bone models 
representing the pre and post-operative geometry were obtained from 
pre and post-operative CT images using Mimics (Materialize, Leuven, 
Belgium). The insertion point of the medial and lateral collateral 
ligament, patellar and posterior cruciate ligament were defined in the 
pre-operative bone models based on the centroid of the respective 
attachments. Relevant soft-tissue attachment sites were transferred to 
the post-operative model by rigidly registering the corresponding 3D 
bone models. The defined bone and soft-tissue geometries were 
assembled with the TKA components following the surgical cuts and 
positioned in accordance with the reference position of the provided 
OpenSim model. For further FEA simulations, bone models were 
removed from the assembly to reduce the computational cost. For each 
component of the assembly, the material models and properties were 
based on the literature [6,7]. The tibial component, underlying the 
tibial tray, was fixed. The ligaments were pre-strained [8]. Muscle 
force directions and attachment points were calculated based on the 
static optimization results from step 3 [9]. The contributions of the 
four quadriceps muscles were combined into one single equivalent 
force and point of application on the patella, whereas the knee flexor 
forces were separately applied to the femur. 
During the consequent explicit dynamic FEA (Abaqus 6.12-1, 
Dassault Système, France), the varus/valgus and internal/external 
rotation of the femoral component was calculated while the 
flexion/extension was constrained to follow the knee flexion angle 
obtained from the inverse kinematics (step1). The patella was free to 
move. 
 FEA-based kinematics of the unconstrained DOFs of the patella 
and of the femoral component (step 4) was compared to the initial 
kinematics that served as input to the muscle force calculation (step 3). 
An iterative procedure between both steps was implemented until 
convergence of the unconstrained kinematics. At convergence, the 
associated TF contact forces are saved. 
 
RESULTS AND DISCUSSION 
 Figure 2 shows TF contact forces for the first cycle of gc1 (a) and 
gc2 (b). 
The first novelty of our methodology lies in the successful integration 
of and iteration between multi-body simulations and FEA. Secondly, 
the applied Kalman smoother algorithm results in more accurate 
estimates of the DOFs with a limited range of motion. Finally, the 
EMG-constrained static optimization procedure combines the 
advantages of optimization procedures and EMG-driven simulations. 
It calculates individual muscle forces for a large number of muscles 
while accounting for the experimentally measured muscle activation 
patterns. Further steps in our analysis include the assessment of the 
subject-specific ligament properties based on the laxity tests and the 
validation of the predicted kinematics based on the fluoroscopy 
images. 
 Computational methods can complement surgical decision 
making and patient rehabilitation; this potential is recognized by those 
developing such methods although a significant portion of the clinical 
community remains skeptical.  The onus is on us to convince our 
critics about the validity of the model with respect to relevant output 
parameters. The Grand Challenge provides such an opportunity. We 
hope this effort will be continued and that extended data collection in 
patients with an instrumented knee implant will allow us to validate 
the tools we developed for MRI-based musculoskeletal modeling and 
MT parameter estimation based on extended dynamometer 
experiments. 
Figure 2: Lat (blue) and Med (red) TF contact forces: (a) gc1, (b) gc2. 
 
ACKNOWLEDGEMENTS 
We gratefully acknowledge project G.0395.09 of the Research 
Foundation - Flanders and KU Leuven’s Interdisciplinary Research 
Program IDO/07/12. 
 
REFERENCES 
[1] Fregly B.J., Besier T.F., Lloyd D.G., Delp S.L.,  Banks S.A., 
Pandy M.G., D’Lima D.D., 2012, “Grand Challenge Competition 
to Predict in Vivo Knee Loads”, Journal of Orthopaedic 
Research,  30, pp. 503-513. 
[2] Delp S., Anderson F., Arnold A., Loan P., A. H., John C., 
Guendelman E., Thelen D., 2007, “OpenSim: Open-source 
software to create and analyze dynamic simulations of 
movement.” IEEE Transactions on Biomedical Engineering, 
54(11), pp.1940-1950. 
[3] Delp, S.L., Loan, J.P., Hoy M.G., Zajac F.E., Topp E.L., Rosen 
J.M., 1990, “An interactive graphics-based model of the lower 
extremity to study orthopaedic surgical procedures.” IEEE Trans 
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[4] Li G., Papannagari R., Nha K.W., Defrate L.E., Gill T.J., Rubash 
H.E., 2007, “The coupled motion of the femur and patella during 
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Engineering, 129(6): p. 937-43. 
[5] De Groote F., De Laet, T., Jonkers, I., De Schutter, J., 2008, 
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Biomechanics, 41 (16), pp. 3390-3398.  
[6] Bendjaballah M.Z., Shirazi-Adl A., and Zukor D.J., 1995, 
“Biomechanics of the human knee joint in compression: 
reconstruction, mesh generation and finite element analysis”, The 
knee, 2(2), pp. 69–79. 
[7] Penrose J.M.T., Holt G.M., Beaugonin M., and Hose D.R., 2002, 
“Development of an accurate three-dimensional finite element 
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[8] Pena E., Calvo B., Martinez M.A., Doblare M., 2005, “A three-
dimensional finite element analysis of the combined behavior of 
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of Biomechanics, 39(9), pp.1686-701.  
[9] Modenese L., 2012. Hip contact force prediction using a 
musculoskeletal model of the lower limb. PhD Thesis, Imperial 
College London, London. 
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Tibio-Femoral Contact Force During Gait: An Iterative Method Using EMG-Constrained Multi-Body Simulation and Finite Element Analysis

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Tibio-Femoral Contact Force During Gait: An Iterative Method Using EMG-Constrained Multi-Body Simulation and Finite Element Analysis

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