TY - JOUR
T1 - Bayesian parameter estimation of ligament properties based on tibio-femoral kinematics during squatting
AU - Bartsoen, Laura
AU - Faes, Matthias G.R.
AU - Andersen, Michael Skipper
AU - Wirix-Speetjens, Roel
AU - Moens, David
AU - Jonkers, Ilse
AU - Sloten, Jos Vander
N1 - Publisher Copyright:
© 2022 The Authors
PY - 2023/1/1
Y1 - 2023/1/1
N2 - The objective of this study is to estimate the, probably correlated, ligament material properties and attachment sites in a highly non-linear, musculoskeletal knee model based on kinematic data of a knee rig experiment for seven specific specimens. Bayesian parameter estimation is used to account for uncertainty in the limited experimental data by optimization of a high dimensional input parameter space (50 parameters) consistent with all probable solutions. The set of solutions accounts for physiologically relevant ligament strain (ϵ<6%). The transitional Markov Chain Monte Carlo algorithm was used. Alterations to the algorithm were introduced in order to avoid premature convergence. To perform the parameter estimation with feasible computational cost, a surrogate model of the knee model was trained. Results show that there is a large intra- and inter-specimen variability in ligament properties, and that multiple sets of ligament properties fit the experimentally measured tibio-femoral kinematics. Although all parameters were allowed to vary significantly, large interdependence is only found between the reference strain and attachment sites. The large variation between specimens and interdependence between reference strain and attachment sites within one specimen, show the inability to identify a small range of ligament properties representative for the patient population. To limit ligament properties uncertainty in clinical applications, research will need to invest in establishing patient-specific uncertainty ranges and/or accurate in vivo measuring methods of the attachment sites and reference strain and/or alternative (combinations of) movements that would allow identifying a unique solution.
AB - The objective of this study is to estimate the, probably correlated, ligament material properties and attachment sites in a highly non-linear, musculoskeletal knee model based on kinematic data of a knee rig experiment for seven specific specimens. Bayesian parameter estimation is used to account for uncertainty in the limited experimental data by optimization of a high dimensional input parameter space (50 parameters) consistent with all probable solutions. The set of solutions accounts for physiologically relevant ligament strain (ϵ<6%). The transitional Markov Chain Monte Carlo algorithm was used. Alterations to the algorithm were introduced in order to avoid premature convergence. To perform the parameter estimation with feasible computational cost, a surrogate model of the knee model was trained. Results show that there is a large intra- and inter-specimen variability in ligament properties, and that multiple sets of ligament properties fit the experimentally measured tibio-femoral kinematics. Although all parameters were allowed to vary significantly, large interdependence is only found between the reference strain and attachment sites. The large variation between specimens and interdependence between reference strain and attachment sites within one specimen, show the inability to identify a small range of ligament properties representative for the patient population. To limit ligament properties uncertainty in clinical applications, research will need to invest in establishing patient-specific uncertainty ranges and/or accurate in vivo measuring methods of the attachment sites and reference strain and/or alternative (combinations of) movements that would allow identifying a unique solution.
KW - Bayesian parameter estimation
KW - Ligament properties
KW - Musculoskeletal knee model
UR - http://www.scopus.com/inward/record.url?scp=85134427531&partnerID=8YFLogxK
U2 - 10.1016/j.ymssp.2022.109525
DO - 10.1016/j.ymssp.2022.109525
M3 - Journal article
AN - SCOPUS:85134427531
SN - 0888-3270
VL - 182
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
M1 - 109525
ER -