Computational Model of the Human Elbow and Forearm: Application to Complex Varus Instability

Spratley, E. Meade; Wayne, Jennifer S.

doi:10.1007/s10439-010-0224-y

Cited by 25 publications

(24 citation statements)

References 24 publications

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“…However, these models typically ignored the ligament contribution in joint modeling. Although some studies incorporated the ligament effect in the model (Fisk & Wayne, 2009;Spratley & Wayne, 2011), these studies ignored cartilages, ligament non-linear property and ligament wrapping around the bone. Modeling cartilage in the joints, wrapping the ligament around the bone, and incorporating the ligament non-linearity in the model is the unique work presented in this study.…”

Section: Discussionmentioning

confidence: 99%

“…The linear region increases at a constant rate with the application of forces to the ligament. Table 3.3 came from published literature (Fisk & Wayne, 2009;Regan, Korinek, Morrey, & An, 1991;Spratley & Wayne, 2011) A damping coefficient of 0.5 Ns/mm was included in each spring element to remove the possibility of high frequency vibration during simulation (Guess, 2012). The triceps tendon was also modeled as a single bundle nonlinear spring damper element using a stiffness parameter (0.2 N/mm) obtained from Tate (2012).…”

Section: Multibody Model Formulationsmentioning

confidence: 99%

“…Numerous studies of computational modeling have been developed to investigate joint behavior (Buchanan, Delp, & Solbeck, 1998;Fisk & Wayne, 2009;Garner & Pandy, 2001;Gonzalez et al, 1999;Gonzalez et al, 1996;Holzbaur et al, 2005;Kwak et al, 2000;Lemay & Crago, 1996;Raikova, 1992;Schuind et al, 1991;Spratley & Wayne, 2011;Triolo et al, 2001). Gonzalez et al (1996) developed a computational elbow model to investigate the elbow joint movement, relationship among muscle excitation patterns, and to determine the effects of forearm and elbow position on the recruitment of individual muscles during ballistic movements.…”

Section: Introductionmentioning

confidence: 99%

“…These presumptions decrease the validity of model results, and prevent investigation of ligament function and joint laxity (Fisk, 2007). Fewer biomechanical models of the elbow joint have investigated the ligamentous constraints, articular surface contact, and muscle loading effect on joint stability, but have not included wrapping of ligaments around bone, the non-linear ligament 'toe' region, and articular cartilage contribution (Fisk & Wayne, 2009;Spratley & Wayne, 2011). Irrespective of any modeling assumption, some models have been limited by model validation (Chao et al, 2007;Delp & Loan, 1995;Fernandez & Pandy, 2006;Kwak et al, 2000;Woo et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

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Development and validation of a computational multibody model of the elbow joint

Rahman

2014

Advances in biomechanics and applications

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Computational multibody models of the elbow joint can provide a powerful tool to study joint biomechanics, examine muscle and ligament function, soft tissue loading, and the effects of joint trauma. Such models can reduce the cost of expensive experimental testing and can predict some parameters that are difficult to investigate experimentally, such as forces within ligaments and contact forces between cartilage covered bones. These parameters can assist surgeons and other investigators to develop better treatments for elbow injuries and thereby increase patient care. Biomechanical computational models of the elbow exist in the literature, but these models are typically limited in their applicability by artificially constraining the joint (e.g. modeling the elbow as a hinge joint), prescribing specific kinematics, simplifying ligament characteristics or ignoring cartilage geometries.The purpose of this thesis was to develop anatomically correct subject specific computational multibody models of elbow joints and validate these models against experimental data. In these models, the joints were constrained by three-dimensional deformable contacts between articulating geometries, passive muscle loading, and multiple bundles of non-linear ligaments wrapped around the bones. iv In this approach, three-dimensional bone geometries for the model were constructed from volume images generated by computed tomography (CT) scans obtained from cadaver elbows. The ligaments and triceps tendon were modeled as spring-damper elements with non-linear stiffness. Articular cartilage was represented as uniform thickness solids covering the articulating bone surfaces. Finally, the model was validated by placing the cadaver elbows in a mechanical testing apparatus and comparing predicted kinematics and triceps tendon forces to experimentally measured values. A small improvement in predicted kinematics was observed compared to experimental values when the lateral ulnar collateral and annular ligament were wrapped around the bone. Some reductions of RMS error were also observed when a non-linear toe region was modeled in the ligament compared to models that had only a linear force-displacement relationship. None of these changes were statistically significant (ANOVA p-value was greater than 0.05).

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Section: Discussionmentioning

confidence: 99%

Section: Multibody Model Formulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development and validation of a computational multibody model of the elbow joint

Rahman

2014

Advances in biomechanics and applications

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“…The multibody (MB) method has been successfully used to model joint behavior [6–12], especially as it pertains to joint kinematics and contact forces. Recently anatomically based MB joint models have been implemented in body level musculoskeletal simulations [1,7].…”

Section: Introductionmentioning

confidence: 99%

Application of neural networks for the prediction of cartilage stress in a musculoskeletal system

Pulasani

Derakhshani

et al. 2013

Biomedical Signal Processing and Control

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Traditional finite element (FE) analysis is computationally demanding. The computational time becomes prohibitively long when multiple loading and boundary conditions need to be considered such as in musculoskeletal movement simulations involving multiple joints and muscles. Presented in this study is an innovative approach that takes advantage of the computational efficiency of both the dynamic multibody (MB) method and neural network (NN) analysis. A NN model that captures the behavior of musculoskeletal tissue subjected to known loading situations is built, trained, and validated based on both MB and FE simulation data. It is found that nonlinear, dynamic NNs yield better predictions over their linear, static counterparts. The developed NN model is then capable of predicting stress values at regions of interest within the musculoskeletal system in only a fraction of the time required by FE simulation.

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Performance evaluation of surgical techniques for treatment of scapholunate instability in a type II wrist

Leonardo-Diaz

Alonso-Rasgado

Jimenez-Cruz

et al. 2019

Numer Methods Biomed Eng

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We investigated the performance of three tenodesis techniques, modified Brunelli, Corella, and scapholunate axis (SLAM) methods in repairing scapholunate interosseous ligament (SLIL) disruption for a type II wrist using finite element-based virtual surgery and compared the results with those of a previous investigation for a type I wrist. In addition, a comparison of the carpal mechanics of type I and type II wrists was undertaken in order to elucidate the difference between the two types. For the type II wrist, following simulated SLIL disruption, the Corella reconstruction technique provided a superior outcome, restoring dorsal gap, volar gap, and SL angle to within 3.5%, 7.1%, and 8.4%, respectively, of the intact wrist. Moreover, application of the ligament reconstruction techniques did not significantly alter the motion pattern of the type II and type I wrists. For the type I wrist, SLIL disruption resulted in no contact between scaphoid-lunate cartilage articulation, whereas for the type II wrist, some contact was maintained. We conclude that the Corella ligamentous reconstruction technique is best able to restore SL gap, angle, and stability following SL ligament injury for both type II and type I wrists and is able to do so without altering wrist kinematics. Our findings also support the view that type I wrists exhibit row behaviour and type II wrists column behaviour. In addition, our analysis suggests that the extra articulation between the lunate and hamate in a type II wrist may help improve stability following SL ligament injury. Novelty• No previous studies have assessed the performance of scapholunate ligament reconstruction techniques considering the two types (I and II) of wrist.• No previous studies have investigated wrist carpal mechanics both before and after disruption of the SL ligament based on lunate bone morphology.

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Computational Model of the Human Elbow and Forearm: Application to Complex Varus Instability

Cited by 25 publications

References 24 publications

Development and validation of a computational multibody model of the elbow joint

Development and validation of a computational multibody model of the elbow joint

Application of neural networks for the prediction of cartilage stress in a musculoskeletal system

Performance evaluation of surgical techniques for treatment of scapholunate instability in a type II wrist

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