A Computational Model to Predict in Vivo Lower Limb Kinetics and Assess Total Knee Arthroplasty Design Parameters
| Author | : |
| Publisher | : |
| Total Pages | : |
| Release | : 2005 |
| ISBN-13 | : OCLC:60822467 |
| ISBN-10 | : |
| Rating | : 4/5 ( Downloads) |
Download or read book A Computational Model to Predict in Vivo Lower Limb Kinetics and Assess Total Knee Arthroplasty Design Parameters written by and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Evaluating total knee arthroplasty implant design success generally requires many years of patient follow-up studies which are both inefficient and costly. Although computational modeling is utilized during the implant design phase, it has yet to be fully utilized in order to predict the post-implantation kinetics associated with various design parameters. The objective of this study was to construct a three-dimensional computational model of the human lower limb that could predict in vivo kinetics based upon input subject specific kinematics. The model was constructed utilizing Kane's theory of dynamics and applied to two clinical sub-studies. Firstly, axial tibiofemoral forces were compared over a deep knee bend between normal knee subjects and those with implanted knees. Secondly, kinematics were obtained for a sample subject undergoing a deep knee bend, and the amount of femoral rollback experienced by the subject (1.86 mm) was varied in order to evaluate the subsequent change in the axial tibiofemoral contact force and the quadriceps force. The mean axial tibiofemoral contact force was 1.35xBW and 2.99xBW for the normal and implanted subjects, respectively, which was a significant difference (p = 0.0023). The sample subect experienced a decrease in both the axial tibiofemoral contact force (8.97%) and the quadriceps load (−11.84%) with an increase of femoral rollback to −6 mm. A decrease in rollback to 6 mm led to increases in both the contact force (22.45%) and the quadriceps load (27.14%). These initial studies provide evidence that this model accurately predicts in vivo kinetics and that kinetics depend on implant design and patient kinematics.
