Prediction of In Vivo Knee Mechanics During Daily Activities Based on a Musculoskeletal Model Incorporated with a Subject-Specific Knee Joint

基于结合个体特异性膝关节的肌肉骨骼模型预测日常活动中体内膝关节力学

阅读:3

Abstract

The objective of this study was to develop a musculoskeletal model incorporated with a subject-specific knee joint to predict the tibiofemoral contact force (TFCF) during daily motions. For this purpose, 18 healthy participants were recruited to perform the motion data acquisition using synchronized motion capture and force platform systems, and motion simulation based on an improved musculoskeletal model for five daily activities, including normal walking, stair ascent, stair descent, sit-to-stand, and stand-to-sit. The proposed musculoskeletal model included subject-specific models of bones, cartilages, and meniscus, detailed knee ligaments and muscles, deformable elastic contacts, and multiple degrees of freedom (DOFs) of the knee joint. The prediction accuracy was demonstrated by the good agreements of TFCF curves between the model predictions and in vivo measurements for the five activities (RMSE: 0.216~0.311 BW, R(2): 0.928~0.992, and C(E): 0.048~0.141). Based on the validated model, the TFCF on total, medial, and lateral compartments (TFCF(Total), TFCF(Medial), and TFCF(Lateral)) during the five daily activities were predicted. For TFCF(Total), the peak force for stair descent or sit-to-stand was the largest, followed by stair ascent or stand-to-sit, and finally normal walking. For TFCF(Medial), stair descent had the largest peak, followed by stair ascent. There were no significant differences between the peak TFCF(Medial) values of normal walking, sit-to-stand, and stand-to-sit. For TFCF(Lateral), the peak of sit-to-stand was the largest, followed by stand-to-sit or stair descent, and finally normal walking or stair ascent. This study is valuable for further understanding the biomechanics of a healthy knee joint and providing theoretical guidance for the treatment of knee osteoarthritis (KOA).

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。