Research Papers

Applying a Hybrid Experimental-Computational Technique to Study Elbow Joint Ligamentous Stabilizers

[+] Author and Article Information
Danial Sharifi Kia

Department of Mechanical Engineering,
Boston University,
110 Cummington Mall,
Boston, MA 02215
e-mail: dskia@bu.edu

Ryan Willing

Department of Mechanical and Materials
The University of Western Ontario,
1151 Richmond Street
London, ON N6A 5B9, Canada
e-mail: rwilling@uwo.ca

1Corresponding author.

Manuscript received December 12, 2017; final manuscript received March 15, 2018; published online April 4, 2018. Assoc. Editor: Guy M. Genin.

J Biomech Eng 140(6), 061012 (Apr 04, 2018) (7 pages) Paper No: BIO-17-1585; doi: 10.1115/1.4039674 History: Received December 12, 2017; Revised March 15, 2018

Much of our understanding of the role of elbow ligaments to overall joint biomechanics has been developed through in vitro cadaver studies using joint motion simulators. The principle of superposition can be used to indirectly compute the force contributions of ligaments during prescribed motions. Previous studies have analyzed the contribution of different soft tissue structures to the stability of human elbow joints, but have limitations in evaluating the loads sustained by those tissues. This paper introduces a unique, hybrid experimental-computational technique for measuring and simulating the biomechanical contributions of ligaments to elbow joint kinematics and stability. in vitro testing of cadaveric joints is enhanced by the incorporation of fully parametric virtual ligaments, which are used in place of the native joint stabilizers to characterize the contribution of elbow ligaments during simple flexion–extension (FE) motions using the principle of superposition. Our results support previously reported findings that the anterior medial collateral ligament (AMCL) and the radial collateral ligament (RCL) are the primary soft tissue stabilizers for the elbow joint. Tuned virtual ligaments employed in this study were able to restore the kinematics and laxity of elbows to within 2 deg of native joint behavior. The hybrid framework presented in this study demonstrates promising capabilities in measuring the biomechanical contribution of ligamentous structures to joint stability.

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Fig. 1

VIVO 6DoF experimental setup used to evaluate the kinetic/kinematic response of the specimens (a) and VIVOSim simulation environment with virtual ligaments applied to the articulations reconstructed using CT data (b)

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Fig. 2

The framework used in this study to evaluate the contribution of elbow joint soft tissue stabilizers

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Fig. 3

Mean and standard deviation of the VV moments generated by specimens at different levels of ligament resection. Height of the bars represents the average VV moments (valgus in the positive direction and varus in the negative direction) generated by the specimens. The error bars on the positive and negative side represent the standard deviation in recorded valgus and varus moments, respectively. The black horizontal lines on the bars represent the neutral VV moment baseline (the moment generated in the joint in neutral VV position). #p < 0.05: decrease over capsule-cut values, *p < 0.05: decrease over the previous level.

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Fig. 4

Average neutral VV angle of intact specimens during FE motions compared to fully injured joints with virtual ligaments

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Fig. 5

Average change in neutral VV angle of the specimens at different stages of ligament resection

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Fig. 6

Varus/valgus laxity of the specimens at different levels of ligament resection. Height of the bars represents the average VV laxity of the specimens (valgus on the positive side, varus on the negative side) under VV loads. The error bars on the positive and negative sides represent the standard deviation in valgus and varus laxity, respectively. *p < 0.05: increase/decrease over capsule-cut values.

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Fig. 7

Forces generated by virtual ligaments during FE motions under valgus (a) and varus (b) loads



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