A new method for estimating subject‐specific muscle–tendon parameters of the knee joint actuators: a simulation study

Abstract

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.

Neuromusculoskeletal Model Calibration Significantly Affects Predicted Knee Contact Forces for Walking

“…To avoid infeasible initial guesses, we scaled the initial values of optimal muscle fiber lengths and tendon slack lengths taken from the literature [12] following an approach similar to Ref. [34]. Muscle-tendon model inputs included activation, optimal muscle fiber length, and tendon slack length values guessed by the two-level optimization described below and muscle-tendon length and velocity information provided by the final OPENSIM inverse kinematic analyses.…”

Estimation of musculotendon parameters for scaled and subject specific musculoskeletal models using an optimization technique

“…On the other hand, functional methods (Hatze, 1981;Garner and Pandy, 2003;Lloyd and Besier, 2003) generally require collection of a relatively large amount of experimental data, which may not always be feasible. Furthermore, other approaches have only been tested with simulated data (Van Campen et al, 2014) or assume muscle forces known from a previous optimization procedure (Ojeda and Mayo, 2013), so evidence of their performance in experimental conditions is still lacking.…”

“…A common shortcoming of all anthropometric methods is that they focus on a single joint, so considering muscle spanning multiple degrees of freedom (DOFs) as mono-articular (Van Campen et al, 2014), not explicitly treating the case of multi-articular muscles (Manal and Buchanan, 2004) or approaching the issue in a simplified way, for instance just considering motion in the sagittal plane (Winby et al, 2008). A further limitation is that they have only been used in linearly scaled adaptations of existing generic models, and never to generate musculotendon parameters in subject specific models built from medical images.…”

Subject-specific lower extremity modeling: personalization of musculoskeletal models using medical imaging and functional measurements

“…Hence, these methods are still laborious and not feasible to create a personalized model of the complete lower extremity. One option is to optimize muscle-tendon parameters taking into account subject-specific muscle strength and moment-angle relationship based on dynamometry data (Hatze, 1981;Koo et al, 2002;Garner et al, 2003;van Campen et al, 2014), however no feasible application of these methods have been shown yet.…”

“…In particular, muscle-tendon (MT) parameters, which greatly affect model outcomes (Brand et al, 1986;Hoy et al, 1990;Out et al, 1996), are difficult to measure directly, and are known to vary with age, gender and activity (Friederich et al, 1990). Developing efficient methods that allow estimating subject-specific MT parameters represents a significant challenge (Garner et al, 2003;Winby et al, 2008;van Campen et al, 2014). However, when using detailed state-of-the-art models (Klein Horsman et al, 2007;Arnold et al, 2010;Modenese et al, 2011), it is fundamental to clarify which muscles need to be described with greater detail, and what the required accuracy of the measurement techniques should be.…”