Abstract
ABSTRACT Musculoskeletal modeling is an important tool to estimate knee loads. In these models, anatomical muscles are frequently sub-divided to account for wide origin/insertion areas. The specific sub-division 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 sub-divided muscles especially for second-order recruitment criteria.
Highlights
Within multiple areas, such as clinical gait analysis (Zajac et al 2002) and orthopedics (Mellon et al 2013; Marra et al 2015), knowledge of muscle, ligament and contact forces are important
For the same combination of normalization factor and polynomial order, among the evaluated models, the linearly scaled model demonstrated the lowest RMSDs and highest correlation coefficients the differences were minor for the total and the medial contact force
The results showed a nonlinear effect of the normalization factor and an interaction with the polynomial order for the contact forces
Summary
Within multiple areas, such as clinical gait analysis (Zajac et al 2002) and orthopedics (Mellon et al 2013; Marra et al 2015), knowledge of muscle, ligament and contact forces are important. In forward dynamics-based tracking and inverse dynamics-based simulations (Crowninshield 1978; Rasmussen et al 2001), assumptions about how the muscles are recruited are necessary to resolve the muscle redundancy problem (Erdemir et al 2007). This is typically accomplished by introducing an optimality criterion, frequently expressed as minimization of the sum of muscle activities (defined as muscle force divided by muscle strength) to some power (Praagman et al 2006), the maximum muscle activity (Rasmussen et al 2001), energy (Praagman et al 2006) or a weighted least-square (Knarr and Higginson 2015)
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