Prediction of elbow joint contact mechanics in the multibody framework

Rahman, Munsur; Çil, Akın; Stylianou, Antonis P.

doi:10.1016/j.medengphy.2015.12.012

Cited by 14 publications

(15 citation statements)

References 44 publications

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“…A larger sample size would allow more generalized conclusions to be made for clinical applications. However, the modelling techniques, efficiency, and accuracy were successfully tested by the research team for five cadaver studies [ 14 , 24 ] and three studies of live subject musculoskeletal modelling of a lower extremity [ 25 , 26 , 28 ]. Furthermore, the model included only three major muscles that crossed the elbow joint.…”

Section: Discussionmentioning

confidence: 99%

“…This correction factor reduced the error unintentionally introduced by the experimenter when applying a small amount of ligament force. The ligaments were wrapped around the bone to represent their anatomical physiology, and to prevent the penetration of the ligament into the bone [ 24 ].…”

Section: Methodsmentioning

confidence: 99%

“…A custom macro was written in ADAMS to automatically divide the humerus cartilage into discrete hexahedral elements. The macro performance was successfully tested in many of our previous lab studies [ 15 , 24 , 25 , 26 , 27 , 28 , 29 ]. Each cartilage element had an approximate 3 × 3 mm cross-sectional area.…”

Section: Methodsmentioning

confidence: 99%

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Musculoskeletal Model Development of the Elbow Joint with an Experimental Evaluation

et al. 2018

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A dynamic musculoskeletal model of the elbow joint in which muscle, ligament, and articular surface contact forces are predicted concurrently would be an ideal tool for patient-specific preoperative planning, computer-aided surgery, and rehabilitation. Existing musculoskeletal elbow joint models have limited clinical applicability because of idealizing the elbow as a mechanical hinge joint or ignoring important soft tissue (e.g., cartilage) contributions. The purpose of this study was to develop a subject-specific anatomically correct musculoskeletal elbow joint model and evaluate it based on experimental kinematics and muscle electromyography measurements. The model included three-dimensional bone geometries, a joint constrained by multiple ligament bundles, deformable contacts, and the natural oblique wrapping of ligaments. The musculoskeletal model predicted the bone kinematics reasonably accurately in three different velocity conditions. The model predicted timing and number of muscle excitations, and the normalized muscle forces were also in agreement with the experiment. The model was able to predict important in vivo parameters that are not possible to measure experimentally, such as muscle and ligament forces, and cartilage contact pressure. In addition, the developed musculoskeletal model was computationally efficient for body-level dynamic simulation. The maximum computation time was less than 30 min for our 35 s simulation. As a predictive clinical tool, the potential medical applications for this model and modeling approach are significant.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Musculoskeletal Model Development of the Elbow Joint with an Experimental Evaluation

et al. 2018

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“…The bones geometries, derived from database, were pre-assembled in the extended position. A density of 1600 kg/m 3 was used for the osseous components 16 .…”

Section: Methodsmentioning

confidence: 99%

“…The stiffness parameters K for each bundle were defined from literature 5,[17][18][19][20] and are summarized in Table I. The spring parameter εL was assumed to be 0.03 16,[23][24][25][26] . Moreover, a parallel damper with a damping coefficient of 0.5 Ns/mm was added to the formulation: the damping effect does not alter the load-strain relation but helps to remove eventual high frequency noise during the simulation 26 .…”

Section: Methodsmentioning

confidence: 99%

Multibody modelling of ligamentous and bony stabilizers in the human elbow

Terzini

Zanetti

Audenino

et al. 2019

Muscle Ligaments and Tendons J

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The elbow ligamentous and bony structures play essential roles in the joint stability. Nevertheless, the contribution of different structures to joint stability is not yet clear and a comprehensive experimental investigation into the ligament and osseous constraints changes in relation to joint motions would be uphill and somehow unattainable, due to the impossibility of obtaining all the possible configurations on the same specimen. Therefore, a predictive tool of the joint behavior after the loss of retentive structures would be helpful in designing reconstructive surgeries and in pre-operative planning. In this work, a multibody model consisting of bones and non-linear ligamentous structures is presented and validated through comparison with experimental data. An accurate geometrical model was equipped with non-linear ligaments bundles between optimized origin and insertion points. The joint function was simulated according to maneuvers accomplished in published experimental studies which explored the posteromedial rotatory in-stability (PMRI) in coronoid and posterior medial collateral ligament (PB) deficient elbows. Moreover, a complete design of experiments (DOE) was explored, investigating the influence of the elbow flexion degree, of the coronoid process and of the medial collateral ligaments (MCL) structures (anterior and posterior bundles) in the elbow joint opening. The implemented computational model accurately predicted the joint behavior with intact and deficient stabilizing structures at each flexion degree, and highlighted the statistically significant influence of the MCL structures (P<0.05) on the elbow stability. The predictive ability of this multibody elbow joint model let foresee that future investigations under different loading scenarios and injured or surgically reconstructed states could be effectively simulated, helping the ligaments reconstruction optimization in terms of bone tunnel localizations and grafts pre-loading. Level of evidence: V.

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