Probabilistic Finite Element Prediction of the Active Lower Limb Model

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Abstract:

The scope of this paper is to explore the input parameters of a Finite Element (FE) model of an active lower limb that are most influential in determining the size and the shape of the performance envelope of the kinematics and peak contact pressure of the knee tibial insert introduced during a Total Knee Replacement (TKR) surgery. The active lower limb FE model simulates the stair ascent and it provides a more complicated setup than the isolated TKR model which includes the femoral component and the tibial insert. It includes bones, TKR implant, soft tissues and applied forces. Two probabilistic methods are used together with the FE model to generate the performance envelopes and to explore the key parameters: the Monte Carlo Simulation Technique (MCST) and the Response Surface Method (RSM). It is investigated how the uncertainties in a reduced set of 22 input variables of the FE model affect the kinematics and peak contact pressure of the knee tibial insert. The kinematics is reported in the Grood and Suntay system, where all motion is relative to the femoral component of the TKR. Reported tibial component kinematics are tibio-femoral flexion angle, anterior-posterior and medial-lateral displacement, internal-external and varus-valgus rotation (i.e. abduction-adduction), while the reported patella kinematics are patella-femoral flexion angle, medial-lateral shift and medial-lateral tilt. Tibio-femoral and patella-femoral contact pressures are also of interest. Following a sensitivity analysis, a reduced set of input variables is derived, which represent the set of key parameters which influence the performance envelopes. The findings of this work are paramount to the orthopedic surgeons who may want to know the key parameters that can influence the performance of the TKR for a given human activity.

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Advanced Materials Research (Volumes 463-464)

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1285-1290

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February 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] Laz, P.J., Pal S., Fields, A., Petrella, A.J. and Rullkoetter, P. J., Effects of knee simulator loading and alignment variability on predicted implant mechanics: a probabilistic study, J. Ortho. Res., 2006, Vol. 24, 2212-2221.

DOI: 10.1002/jor.20254

Google Scholar

[2] Laz, P.J., Pal, S., Halloran, J.P., Petrella, A.J. and Rullkoetter, P.J., 2006. Probabilistic finite element prediction of knee wear simulator mechanics. Journal of Biomechanics. 39: pp.2303-2310.

DOI: 10.1016/j.jbiomech.2005.07.029

Google Scholar

[3] Grood, E.S. and Suntay, W. J, 1983. A joint coordinate system for the clinical description of three dimensional motions: Application to the knee. J of Biomechanical Engineering. 105: pp.136-144.

DOI: 10.1115/1.3138397

Google Scholar

[4] Prendergast, P.J., Finite element models in tissue mechanics and orthopedic implant design, Clin. Biomech., 1997, Vol. 12, No. 6, 343-366.

DOI: 10.1016/s0268-0033(97)00018-1

Google Scholar

[5] Godest, A.C., Beaugonin, M., Haug, E., Taylor, M., Gregson, P.J., Simulation of a knee joint replacement during gait cycle using explicit finite element analysis. " J. Biomech, 2002, Vol. 35, 267-275.

DOI: 10.1016/s0021-9290(01)00179-8

Google Scholar

[6] Halloran, J.P., Petrella, A.J., Rullkoetter, P.J., Explicit finite element modelling of total knee replacement mechanics,J. Biomech Eng., 2005a, Vol. 38, 323-331.

DOI: 10.1016/j.jbiomech.2004.02.046

Google Scholar

[7] Halloran, J.P., Easley, S.K., Petrella, A.J., Rullkoetter, P.J., Comparison of deformable and elastic foundation finite element simulations for predicting knee replacement mechanics, J. Biomech Eng., 2005b., Vol. 127, 813-818.

DOI: 10.1115/1.1992522

Google Scholar

[8] Zhang, Y., Liu, Q., Reliability-based design of automobile components. Proceedings of the Institute of Mechanical Engineers, Part D., Journal of Automobile Engineering, 2002, Vol. 216(6), 455-471.

DOI: 10.1243/09544070260137390

Google Scholar

[9] Nicolella, D.P., Thacker, B.H., Katoozian, H., Davy, D.T., Probabilistic risk analysis of a cemented hip implant, 2001, ASME Bioengineering Division, Vol. 50, 427-428.

Google Scholar

[10] Browne, M., Langley, R.S., Gregson, P.J., Reliability theory for load bearing biomedical implants, 1999, Biomaterials, Vol. 20, 1285-1292.

DOI: 10.1016/s0142-9612(99)00027-7

Google Scholar

[11] Dar, F.H., Meakin, J.R., Aspden, R.M., Statistical methods in finite element analysis Journal of Biomechanics, 2002, Vol. 35(9), 1155-1161.

DOI: 10.1016/s0021-9290(02)00085-4

Google Scholar

[12] Metropolis, N. and Ulam, S., The Monte Carlo Method, J. Amer. Stat. Assoc., 1949, Vol. 44, No. 247, 335-341.

Google Scholar

[13] Fishman, G.S., Monte Carlo: Concepts, Algorithms, and Applications, 1995, Springer Verlag, New York.

Google Scholar

[14] Isukapalli, S.S., Balakrishnan, S. and Georgopoulos, P.G., Computationally efficient uncertainty propagation and reduction using the stochastic response surface method, 43rd IEEE Conference on Decision and Control, 2004, Bahamas, US.

DOI: 10.1109/cdc.2004.1430381

Google Scholar

[15] Arsene, C.T.C., Strickland, M.A., Laz, P.J., Taylor, M. Comparison of two probabilistic methods for finite element analysis of total knee replacement,. Proceedings of the 8th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering, CMBBE 2008, February 2008, ISBN 978-0-9562121-0-8.

DOI: 10.1080/10255840903476463

Google Scholar