How complex should lower-limb joint models be for subject-specific musculoskeletal modeling applications?

2015 ◽  
Vol 42 ◽  
pp. S64-S65
Author(s):  
L. Pitto ◽  
G. Valente ◽  
R. Stagni ◽  
F. Taddei
2015 ◽  
Vol 48 (16) ◽  
pp. 4198-4205 ◽  
Author(s):  
Giordano Valente ◽  
Lorenzo Pitto ◽  
Rita Stagni ◽  
Fulvia Taddei

2017 ◽  
Vol 139 (9) ◽  
Author(s):  
Michael Skipper Andersen ◽  
Mark de Zee ◽  
Michael Damsgaard ◽  
Daniel Nolte ◽  
John Rasmussen

Knowledge of the muscle, ligament, and joint forces is important when planning orthopedic surgeries. Since these quantities cannot be measured in vivo under normal circumstances, the best alternative is to estimate them using musculoskeletal models. These models typically assume idealized joints, which are sufficient for general investigations but insufficient if the joint in focus is far from an idealized joint. The purpose of this study was to provide the mathematical details of a novel musculoskeletal modeling approach, called force-dependent kinematics (FDK), capable of simultaneously computing muscle, ligament, and joint forces as well as internal joint displacements governed by contact surfaces and ligament structures. The method was implemented into the anybody modeling system and used to develop a subject-specific mandible model, which was compared to a point-on-plane (POP) model and validated against joint kinematics measured with a custom-built brace during unloaded emulated chewing, open and close, and protrusion movements. Generally, both joint models estimated the joint kinematics well with the POP model performing slightly better (root-mean-square-deviation (RMSD) of less than 0.75 mm for the POP model and 1.7 mm for the FDK model). However, substantial differences were observed when comparing the estimated joint forces (RMSD up to 24.7 N), demonstrating the dependency on the joint model. Although the presented mandible model still contains room for improvements, this study shows the capabilities of the FDK methodology for creating joint models that take the geometry and joint elasticity into account.


2016 ◽  
Vol 139 (1) ◽  
Author(s):  
Laurent Dumas ◽  
Tamara El Bouti ◽  
Didier Lucor

Cardiovascular diseases are currently the leading cause of mortality in the population of developed countries, due to the constant increase in cardiovascular risk factors, such as high blood pressure, cholesterol, overweight, tobacco use, lack of physical activity, etc. Numerous prospective and retrospective studies have shown that arterial stiffening is a relevant predictor of these diseases. Unfortunately, the arterial stiffness distribution across the human body is difficult to measure experimentally. We propose a numerical approach to determine the arterial stiffness distribution of an arterial network using a subject-specific one-dimensional model. The proposed approach calibrates the optimal parameters of the reduced-order model, including the arterial stiffness, by solving an inverse problem associated with the noninvasive in vivo measurements. An uncertainty quantification analysis has also been carried out to measure the contribution of the model input parameters variability, alone or by interaction with other inputs, to the variation of clinically relevant hemodynamic indices, here the arterial pulse pressure. The results obtained for a lower limb model, demonstrate that the numerical approach presented here can provide a robust and subject-specific tool to the practitioner, allowing an early and reliable diagnosis of cardiovascular diseases based on a noninvasive clinical examination.


2020 ◽  
Vol 81 ◽  
pp. 107-108
Author(s):  
N. Golfeshan ◽  
H. Barnamehei ◽  
M. Torabigoudarzi ◽  
M. Karimidastjerdi ◽  
A. Panahi ◽  
...  

2020 ◽  
Vol 17 (2) ◽  
pp. 026029 ◽  
Author(s):  
Dharmendra Gurve ◽  
Denis Delisle-Rodriguez ◽  
Maria Romero-Laiseca ◽  
Vivianne Cardoso ◽  
Flavia Loterio ◽  
...  

2010 ◽  
Vol 25 (1) ◽  
pp. 88-94 ◽  
Author(s):  
K. Oberhofer ◽  
N.S. Stott ◽  
K. Mithraratne ◽  
I.A. Anderson

Biomechanics ◽  
2021 ◽  
Vol 1 (3) ◽  
pp. 293-306
Author(s):  
Sentong Wang ◽  
Kazunori Hase ◽  
Susumu Ota

Finite element musculoskeletal (FEMS) approaches using concurrent musculoskeletal and finite element models driven by motion data such as marker-based motion trajectory can provide insight into the interactions between the knee joint secondary kinematics, contact mechanics, and muscle forces in subject-specific biomechanical investigations. However, these data-driven FEMS systems have a major disadvantage that makes them challenging to apply in clinical environments, i.e., they require expensive and inconvenient equipment for data acquisition. In this study, we developed an FEMS model of the lower limb driven solely by inertial measurement unit sensors that include the tissue geometries of the entire knee joint, and that combine modeling of 16 muscles into a single framework. The model requires only the angular velocities and accelerations measured by the sensors as input. The target outputs (knee contact mechanics, secondary kinematics, and muscle forces) are predicted from the convergence results of iterative calculations of muscle force optimization and knee contact mechanics. To evaluate its accuracy, the model was compared with in vivo experimental data during gait. The maximum contact pressure (11.3 MPa) occurred on the medial side of the cartilage at the maximum loading response. The developed framework combines measurement convenience and accurate modeling, and shows promise for clinical applications aimed at understanding subject-specific biomechanics.


2019 ◽  
Vol 13 (4) ◽  
Author(s):  
Emerson Paul Grabke ◽  
Kei Masani ◽  
Jan Andrysek

Abstract Many individuals with lower limb amputations or neuromuscular impairments face mobility challenges attributable to suboptimal assistive device design. Forward dynamic modeling and simulation of human walking using conventional biomechanical gait models offer an alternative to intuition-based assistive device design, providing insight into the biomechanics underlying pathological gait. Musculoskeletal models enable better understanding of prosthesis and/or exoskeleton contributions to the human musculoskeletal system, and device and user contributions to both body support and propulsion during gait. This paper reviews current literature that have used forward dynamic simulation of clinical population musculoskeletal models to perform assistive device design optimization using optimal control, optimal tracking, computed muscle control (CMC) and reflex-based control. Musculoskeletal model complexity and assumptions inhibit forward dynamic musculoskeletal modeling in its current state, hindering computational assistive device design optimization. Future recommendations include validating musculoskeletal models and resultant assistive device designs, developing less computationally expensive forward dynamic musculoskeletal modeling methods, and developing more efficient patient-specific musculoskeletal model generation methods to enable personalized assistive device optimization.


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