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 weigh… Show more
“…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).…”
Section: Discussionsupporting
confidence: 78%
“…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.…”
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. 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).…”
Section: Discussionsupporting
confidence: 78%
“…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.…”
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Can a reduction approach predict reliable joint contact and musculo-tendon 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.…”
Mechanical devices are common treating methods for knee osteoarthritis. It has the purpose of reducing the internal joint forces and unloading the damaged structure. The reduction is often achieved by alterations in the frontal plan, shifting the contact force from one compartment to the other, leaving the total compressive force unchanged. The aim of this study was to investigate how internal knee joint forces depend on applied external moments during gait. Musculoskeletal models of the gait of 10 healthy subjects were developed in the AnyBody Modelling System and used to simulate applied joint moments about different axes (load cases), each with the magnitude to compensate the net moment about the respective axis by a specified percentage. For each load case, the total, medial and lateral knee compressive force were computed and compared with a baseline case with no external moments applied. Among the investigated moments, hip flexion-extension, knee flexion-extension and ankle plantarflexiondorsiflexion moment compensations have the most positive impact on the total knee joint compressive force, and combining the 3, each with a 40% compensation of the muscle moments, reduced the first peak by 23.6%, the second by 30.6% and the impulse by 28.6% with respect to no applied moments.
ARTICLE HISTORY
“…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].…”
We propose a method for automatic segmentation of individual muscles from a clinical CT. The method uses Bayesian convolutional neural networks with the U-Net architecture, using Monte Carlo dropout that infers an uncertainty metric in addition to the segmentation label. We evaluated the performance of the proposed method using two data sets: 20 fully annotated CTs of the hip and thigh regions and 18 partially annotated CTs that are publicly available from The Cancer Imaging Archive (TCIA) database. The experiments showed a Dice coefficient (DC) of 0.891±0.016 (mean±std) and an average symmetric surface distance (ASD) of 0.994±0.230 mm over 19 muscles in the set of 20 CTs. These results were statistically significant improvements compared to the state-of-the-art hierarchical multi-atlas method which resulted in 0.845±0.031 DC and 1.556±0.444 mm ASD. We evaluated validity of the uncertainty metric in the multi-class organ segmentation problem and demonstrated a correlation between the pixels with high uncertainty and the segmentation failure. One application of the uncertainty metric in activelearning is demonstrated, and the proposed query pixel selection method considerably reduced the manual annotation cost for expanding the training data set. The proposed method allows an accurate patient-specific analysis of individual muscle shapes in a clinical routine. This would open up various applications including personalization of biomechanical simulation and quantitative evaluation of muscle atrophy.
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