RESEARCH ARTICLE
Computational Knee Ligament Modeling Using Experimentally Determined Zero-Load Lengths
Katherine H Bloemker1, *, Trent M Guess1, Lorin Maletsky2, Kevin Dodd2
Article Information
Identifiers and Pagination:
Year: 2012Volume: 6
First Page: 33
Last Page: 41
Publisher ID: TOBEJ-6-33
DOI: 10.2174/1874120701206010033
Article History:
Received Date: 1/1/2012Revision Received Date: 25/2/2012
Acceptance Date: 27/2/2012
Electronic publication date: 2/4/2012
Collection year: 2012
open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
Abstract
This study presents a subject-specific method of determining the zero-load lengths of the cruciate and collateral ligaments in computational knee modeling. Three cadaver knees were tested in a dynamic knee simulator. The cadaver knees also underwent manual envelope of motion testing to find their passive range of motion in order to determine the zero-load lengths for each ligament bundle. Computational multibody knee models were created for each knee and model kinematics were compared to experimental kinematics for a simulated walk cycle. One-dimensional non-linear spring damper elements were used to represent cruciate and collateral ligament bundles in the knee models. This study found that knee kinematics were highly sensitive to altering of the zero-load length. The results also suggest optimal methods for defining each of the ligament bundle zero-load lengths, regardless of the subject. These results verify the importance of the zero-load length when modeling the knee joint and verify that manual envelope of motion measurements can be used to determine the passive range of motion of the knee joint. It is also believed that the method described here for determining zero-load length can be used for in vitro or in vivo subject-specific computational models.
