Musculoskeletal (MS) models should be able to integrate patient-specific MS architecture and undergo thorough validation prior to their introduction into clinical practice. We present a methodology to develop subject-specific models able to simultaneously predict muscle, ligament, and knee joint contact forces along with secondary knee kinematics. The MS architecture of a generic cadaver-based model was scaled using an advanced morphing technique to the subject-specific morphology of a patient implanted with an instrumented total knee arthroplasty (TKA) available in the fifth “grand challenge competition to predict in vivo knee loads” dataset. We implemented two separate knee models, one employing traditional hinge constraints, which was solved using an inverse dynamics technique, and another one using an 11-degree-of-freedom (DOF) representation of the tibiofemoral (TF) and patellofemoral (PF) joints, which was solved using a combined inverse dynamic and quasi-static analysis, called force-dependent kinematics (FDK). TF joint forces for one gait and one right-turn trial and secondary knee kinematics for one unloaded leg-swing trial were predicted and evaluated using experimental data available in the grand challenge dataset. Total compressive TF contact forces were predicted by both hinge and FDK knee models with a root-mean-square error (RMSE) and a coefficient of determination (R²) smaller than 0.3 body weight (BW) and equal to 0.9 in the gait trial simulation and smaller than 0.4 BW and larger than 0.8 in the right-turn trial simulation, respectively. Total, medial, and lateral TF joint contact force predictions were highly similar, regardless of the type of knee model used. Medial (respectively lateral) TF forces were over- (respectively, under-) predicted with a magnitude error of M < 0.2 (respectively > −0.4) in the gait trial, and under- (respectively, over-) predicted with a magnitude error of M > −0.4 (respectively < 0.3) in the right-turn trial. Secondary knee kinematics from the unloaded leg-swing trial were overall better approximated using the FDK model (average Sprague and Geers' combined error C = 0.06) than when using a hinged knee model (C = 0.34). The proposed modeling approach allows detailed subject-specific scaling and personalization and does not contain any nonphysiological parameters. This modeling framework has potential applications in aiding the clinical decision-making in orthopedics procedures and as a tool for virtual implant design.

This article describes the development of a musculoskeletal model of the human lumbar spine with focus on back muscles. It includes data from literature in a structured form. To review the anatomy and biomechanics of the back muscles related to the lumbar spine with relevance for biomechanical modeling. To reduce complexity, muscle units have been incorporated in an abridged manner, reducing their actions more or less to a single force equivalent. In early models of the lumbar spine, this may have been a necessary step to reduce complexity and, thereby, calculation time. The muscles of the spine are well described in the literature, but mainly qualitatively. Most of the literature provides a description of the structures without precise data of fiber length, muscle length, cross-sectional areas, moment arms, forces, etc. The predicted output of musculoskeletal models is very much dependent on the input parameters. The information needed to improve models consists of better approximations of the attachments to the vertebrae, and more precise data. Review of literature. The predicted output of musculoskeletal models is very much dependent on the input parameters. Moderate changes in the assumed muscle line-of-action (i.e., moment arm) could substantially alter the magnitudes of predicted muscle and spinal forces, while the choice of optimization formulation is less sensitive. Input parameters, moment arms, as well as physiologic cross-sectional areas have a profound effect on the predicted muscle forces. Therefore, it is important to choose the values for moment arm and physiologic cross-sectional area carefully because they are essential input parameters to biomechanical models.

This paper introduces a general optimisation-based method for identification of biomechanically relevant parameters in kinematically determinate and over-determinate systems from a given motion. The method is designed to find a set of parameters that is constant over the time frame of interest as well as the time-varying system coordinates, and it is particularly relevant for biomechanical motion analysis where the system parameters can be difficult to accurately determine by direct measurements. Although the parameter identification problem results in a large-scale optimisation problem, we show that, due to a special structure in the linearised Karush–Kuhn–Tucker optimality conditions, the solution can be found very efficiently. The method is applied to a set of test problems relevant for gait analysis. These involve determining the local coordinates of markers placed on the model, segment lengths and joint axes of rotation from both gait and range of motion experiments.