Ulna-humerus contact mechanics: Finite element analysis and experimental measurements using a tactile pressure sensor

Renani, Mohsen Sharifi; Rahman, Munsur; Çil, Akın; Stylianou, Antonis P.

doi:10.1016/j.medengphy.2017.08.010

Cited by 12 publications

(9 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our models were based on rigid body dynamics. However, future studies will also optimize contact parameters and discretized cartilage size by matching a multibody cartilage model with a finite element model of the same cartilage [35,36]. Even with this limitation, our maximum contact pressures were close in range (0.5–5 Mpa) to the values reported in the literature [17,37].…”

Section: Discussionmentioning

confidence: 84%

Medial Collateral Ligament Deficiency of the Elbow Joint: A Computational Approach

2018

Self Cite

View full text Add to dashboard Cite

Computational elbow joint models, capable of simulating medial collateral ligament deficiency, can be extremely valuable tools for surgical planning and refinement of therapeutic strategies. The objective of this study was to investigate the effects of varying levels of medial collateral ligament deficiency on elbow joint stability using subject-specific computational models. Two elbow joint models were placed at the pronated forearm position and passively flexed by applying a vertical downward motion on humeral head. The models included three-dimensional bone geometries, multiple ligament bundles wrapped around the joint, and the discretized cartilage representation. Four different ligament conditions were simulated: All intact ligaments, isolated medial collateral ligament (MCL) anterior bundle deficiency, isolated MCL posterior bundle deficiency, and complete MCL deficiency. Minimal kinematic differences were observed for isolated anterior and posterior bundle deficient elbows. However, sectioning the entire MCL resulted in significant kinematic differences and induced substantial elbow instability. Joint contact areas were nearly similar for the intact and isolated posterior bundle deficiency. Minor differences were observed for the isolated anterior bundle deficiency, and major differences were observed for the entire MCL deficiency. Complete elbow dislocations were not observed for any ligament deficiency level. As expected, during isolated anterior bundle deficiency, the remaining posterior bundle experiences higher load and vice versa. Overall, the results indicate that either MCL anterior or posterior bundle can provide anterior elbow stability, but the anterior bundle has a somewhat bigger influence on joint kinematics and contact characteristics than posterior one. A study with a larger sample size could help to strengthen the conclusion and statistical significant.

show abstract

Section: Discussionmentioning

confidence: 84%

Medial Collateral Ligament Deficiency of the Elbow Joint: A Computational Approach

2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, a decrease in mean contact pressure was considered to be indicative of a general decrease in joint contact stresses, which is aligned with clinical goal of the PAO surgery. Similar approaches have been adopted in FE analysis performed for the understanding of the hip and elbow joint contact mechanics [ 21 , 35 ].…”

Section: Discussionmentioning

confidence: 99%

Computer-Assisted Optimization of the Acetabular Rotation in Periacetabular Osteotomy Using Patient’s Anatomy-Specific Finite Element Analysis

Park¹,

Lee

Chen

et al. 2018

Applied Bionics and Biomechanics

View full text Add to dashboard Cite

Periacetabular osteotomy (PAO) is a complex surgical procedure to restore acetabular coverage in the dysplastic hip, and the amount of acetabular rotation during PAO plays a key role. Using computational simulations, this study assessed the optimal direction and amount of the acetabular rotation in three dimensions for a patient undergoing PAO. Anatomy-specific finite element (FE) models of the hip were constructed based on clinical CT images. The calculated acetabular rotation during PAO were 9.7°, 18°, and 4.3° in sagittal, coronal, and transverse planes, respectively. Based on the actual acetabular rotations, twelve postoperative FE models were generated. An optimal position was found by gradually varying the amount of the acetabular rotations in each anatomical plane. The coronal plane was found to be the principal rotational plane, which showed the strongest effects on joint contact pressure compared to other planes. It is suggested that rotation in the coronal plane of the osteotomized acetabulum is one of the primary surgical parameters to achieve the optimal clinical outcome for a given patient.

show abstract

“…Optimization and design of the experiment approach were used to determine the contact parameters and the size of discretized cartilage elements from a cadaver study ( Table 1 ). The optimization was done in such a way that the maximum contact pressure and contact area errors were minimized between a multibody model and an identically loaded finite element model [ 27 , 30 ].…”

Section: Methodsmentioning

confidence: 99%

Musculoskeletal Model Development of the Elbow Joint with an Experimental Evaluation

et al. 2018

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Ulna-humerus contact mechanics: Finite element analysis and experimental measurements using a tactile pressure sensor

Cited by 12 publications

References 42 publications

Medial Collateral Ligament Deficiency of the Elbow Joint: A Computational Approach

Medial Collateral Ligament Deficiency of the Elbow Joint: A Computational Approach

Computer-Assisted Optimization of the Acetabular Rotation in Periacetabular Osteotomy Using Patient’s Anatomy-Specific Finite Element Analysis

Musculoskeletal Model Development of the Elbow Joint with an Experimental Evaluation

Contact Info

Product

Resources

About