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Scaled generic musculoskeletal models are commonly used to drive dynamic simulations of motions. It is however, acknowledged that not accounting for variability in musculoskeletal geometry and musculotendon parameters may confound the simulation results, even when analysing control subjects. This study documents the three-dimensional anatomical variability of musculotendon origins and insertions of 33 lower limb muscles determined based on magnetic resonance imaging in six subjects. This anatomical variability was compared to the musculotendon point location in a generic musculoskeletal model. Furthermore, the sensitivity of muscle forces during gait, calculated using static optimization, to perturbations of the musculotendon point location was analyzed with a generic model. More specific, a probabilistic approach was used: for each analyzed musculotendon point, the three-dimensional location was re-sampled with a uniform Latin hypercube method within the anatomical variability and the static optimization problem was then re-solved for all perturbations. We found that musculotendon point locations in the generic model showed only variable correspondences with the anatomical variability. The anatomical variability of musculotendon point location did affect the calculated muscle forces: muscles most sensitive to perturbations within the anatomical variability are iliacus and psoas. Perturbation of the gluteus medius anterior, iliacus and psoas induces the largest concomitant changes in muscle forces of the unperturbed muscles. Therefore, when creating subject-specific musculoskeletal models, these attachment points should be defined accurately. In addition, the size of the anatomical variability of the musculotendon point location was not related to the sensitivity of the calculated muscle forces.

A new method for the estimation of subject-specific muscle-tendon parameters of the knee actuators based on dynamometry experiments is presented. The algorithm aims at estimating the tendon slack length and the optimal muscle fiber length by minimizing the difference between experimentally reproduced and model-based joint moments. The key innovative features are as follows: (i) the inclusion of a priori physiological knowledge to define a physiologically feasible set, the hot start for the optimization, and constraints for the optimization and (ii) the introduction of a new (affine) transformation of the muscle-tendon parameters, which greatly improves the numerical condition of the optimization. The influence of the initial guess and of measurement noise was studied in a simulation environment, and the performance was compared with that of the method presented earlier by Garner and Pandy for the upper limb. The tendon slack length was estimated for 97.5/63% (extensors/flexors) of all initial guesses within 2% of the ground truth. The optimal fiber length was estimated for 89/90% (extensors/flexors) of all initial guesses within 2% of the ground truth. When 10 Nm measurement noise was added, the mean value of the estimated tendon slack length deviated at most 1.9/1.6% (extensors/flexors) from the ground truth whereas the standard deviations were at most 5.1/3.9%. The mean value of the estimated optimal fiber length deviated at most 4.3/3.0% (extensors/flexors) from the ground truth whereas the standard deviations were at most 10.2/15.5%. In comparison, mean values resulting from the method of Garner and Pandy deviated up to 181% ( ± 123%) and 119% ( ± 30%) from the ground truth for, respectively, optimal fiber length and tendon slack length of rectus femoris. We concluded that the presented method had a low dependency on the initial guess and outperformed the method of Garner and Pandy in terms of accuracy by at least one order of magnitude when parameters were estimated from noisy data. The improvements open new perspectives for subject-specific modelling of muscles and tendons, which is beneficial for the accuracy of human motion simulations.

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This paper analyzes an extended dynamometry setup that aims at obtaining accurate knee joint moments. The main problem of the standard setup is the misalignment of the joint and the dynamometer axes of rotation due to nonrigid fixation, and the determination of the joint axis of rotation by palpation. The proposed approach 1) combines 6-D registration of the contact forces with 3-D motion capturing (which is a contribution to the design of the setup); 2) includes a functional axis of rotation in the model to describe the knee joint (which is a contribution to the modeling); and 3) calculates joint moments by a model-based 3-D inverse dynamic analysis. Through a sensitivity analysis, the influence of the accuracy of all model parameters is evaluated. Dynamics resulting from the extended setup are quantified, and are compared to those provided by the dynamometer. Maximal differences between the 3-D joint moment resulting from the inverse dynamics and measured by the dynamometer were 16.4 N ·m (16.9%) isokinetically and 18.3 N ·m (21.6%) isometrically. The calculated moment is most sensitive to the orientation and location of the axis of rotation. In conclusion, more accurate experimental joint moments are obtained using a model-based 3-D inverse dynamic approach that includes a good estimate of the pose of the joint axis.