Alterations of musculoskeletal models for a more accurate estimation of lower limb joint contact forces during normal gait: A systematic review

Abstract

Musculoskeletal modelling is a methodology used to investigate joint contact forces during a movement. High accuracy in the estimation of the hip or knee joint contact forces can be obtained with subject-specific models. However, construction of subject-specific models remains time consuming and expensive. The purpose of this systematic review of the literature was to identify what alterations can be made on generic (i.e. literature-based, without any subject-specific measurement other than body size and weight) musculoskeletal models to obtain a better estimation of the joint contact forces. The impact of these alterations on the accuracy of the estimated joint contact forces were appraised. The systematic search yielded to 141 articles and 24 papers were included in the review. Different strategies of alterations were found: skeletal and joint model (e.g. number of degrees of freedom, knee alignment), muscle model (e.g. Hill-type muscle parameters, level of muscular redundancy), and optimisation problem (e.g. objective function, design variables, constraints). All these alterations had an impact on joint contact force accuracy, so demonstrating the potential for improving the model predictions without necessarily involving costly and time consuming medical images. However, due to discrepancies in the reported evidence about this impact and despite a high quality of the reviewed studies, it was not possible to highlight any trend defining which alteration had the largest impact.

Can a reduction approach predict reliable joint contact and musculo-tendon forces?

“…This is the level of concordance already reported for more comprehensive muscle geometries (Giroux et al, 2013). The errors in the proximaldistal tibiofemoral contact force fell in the range of RMSEs (0.3 to 0.9 BW) reported in the literature with generic musculoskeletal models (Moissenet et al, 2017). As for the RMSEs on the force peaks, they range between 0.06 and 1.10 BW and match the typical errors (0.1 to 1.7 BW) reported with musculoskeletal models using numerical optimisation (DeMers et al, 2014;Knarr and Higginson, 2015;Thelen et al, 2014).…”

“…Only a preliminary validation (errors on proximal-distal tibiofemoral contact force peaks below 10% during gait) has been reported using the data of one subject with an instrumented prosthesis (Messier et al, 2013). This reported level of error, below 10%, compares favourably with the errors obtained with numerical optimisation (Moissenet et al, 2017). However, this preliminary validation was performed on one subject and was limited to peak values of the proximal-distal tibiofemoral contact force.…”

On the biomechanical relationship between applied hip, knee and ankle joint moments and the internal knee compressive forces

“…There are some limitations and uncertainties related to MS models and several parameters influences the joint loads (Moissenet et al 2017). As shown in this study, the joint loads are highly affected by the surrounding muscles so the chosen muscle parameters have a big impact on the load reduction.…”

Automated Muscle Segmentation from Clinical CT Using Bayesian U-Net for Personalized Musculoskeletal Modeling

“…The computational simulation of human motion using musculoskeletal modeling has been performed in a number of studies to investigate musculo-tendon forces and joint contact forces, which cannot be easily achieved by physical measurements [25], [27], [29]. Recent studies have demonstrated that personalization of model parameters, such as the size of the bones, geometry of the muscles and tendons, and physical properties of the muscle-tendon complex, improves accuracy of the simulation [4], [20], [32]. While the majority of previous studies modeled the musculo-tendon unit as one or multiple lines joining their origin and insertion, including so-called via points in some cases, several recent studies have shown that volumetric models representing subject-specific muscle geometry provide higher accuracy in the simulation [37].…”