How sensitive are predicted muscle and knee contact forces to normalization factors and polynomial order in the muscle recruitment criterion formulation?

Abstract

Musculoskeletal modeling is an important tool to estimate knee loads. In these models, anatomical muscles are frequently subdivided to account for wide origin/insertion areas. The specific subdivision has been shown to affect some muscle recruitment criteria and it has been suggested that normalization factors should be incorporated into models. The primary aim of this study was to investigate the effect of different muscle normalization factors in the muscle recruitment criterion and polynomial order on the estimated muscle and total, medial and lateral knee contact forces during gait. These were evaluated on three different musculoskeletal models with increasing levels of patient-specificity and knee joint model complexity for one subject from the Grand Challenge data set and evaluated against measured forces. The results showed that the introduction of the muscle normalization factors affected the estimated forces and that this effect was most pronounced when a polynomial of order two was applied. Additionally, mainly the second contact force peak was affected. Secondary investigations revealed that the predicted forces can vary substantially as a function of the knee flexor and extensor muscle strength with over one body weight difference in predicted total compressive force between 100% and 40% of the strength. Additionally, the predicted second peak during gait was found to be sensitive to the position of the pelvic skin marker positions in the model. These results imply that caution should be taken when a normalization factor is introduced to account for subdivided muscles especially for second-order recruitment criteria.

12 Degrees of Freedom Muscle Force Driven Fibril-Reinforced Poroviscoelastic Finite Element Model of the Knee Joint

“…Numerous studies have been conducted to investigate the mediolateral distribution of the JCF under different activities or conditions. Most of the approaches have predicted considerably lower JCF on the lateral compartment of the knee joint compared to experimental findings [2], [30], [32], [58]. Similar to those studies, the Gait2392 MSFE model of the study predicted the lowest lateral JCF compared to the other two MSFE models, as well as the Grand Challenge Dataset (Fig.…”

“…In contrast, some studies have shown that a subjectspecific electromyography-informed (EMG-informed) MS model [31] or an EMG-assisted MS model linked with a subject-specific FRPVE FE model [15], both with 1 DoF knee joint MS models can predict the JCF comparable with experiments. Importantly, Andersen (2018) has shown that the inclusion of the subject-specific geometry does not improve the JCF predictions compared to a linearly-scaled MS model [32].…”

Using Musculoskeletal Models to Estimate in vivo Total Knee Replacement Kinematics and Loads: Effect of Differences Between Models

“…The accuracy of the three models in predicting the force transmitted at the knee joint was found comparable with that reported in previous studies (Marra et al, 2015 ; Chen et al, 2016 ; Andersen, 2018 ). The correlation between the prediction and measured values of the model is pretty low for the Arnold's model ( R 2 = 0.22), better for the Lai's model ( R 2 = 0.77), and even better for the Rajagopal's model ( R 2 = 0.80).…”

Automated creation and tuning of personalised muscle paths for OpenSim musculoskeletal models of the knee joint

“…Computational models of the human musculoskeletal system enable researchers to study human biomechanics without invasive and/or expensive experiments. Research focus has been applied to rigid body musculoskeletal modelling tools, e.g., AnyBody Modeling Software AnyBody (AnyBody Technology, Aalborg, Denmark) (Damsgaard et al 2006) or more commonly OpenSim (Delp et al 2007;Seth et al 2018), to estimate internal tissue loading, such as muscle tendon unit (MTU) and joint contact forces (Ackland et al 2011;Andersen 2018;Cleather and Bull 2011;Guess et al 2014;Konrath et al 2017;Modenese et al 2018;Saxby et al 2016;Winby et al 2009). These models typically use generic bone geometries, joint positions and orientations, and MTU pathways that are unlikely to reflect individual anatomy, even when carefully scaled (Davico et al 2019;Kainz et al 2017).…”