Stylianou Antonis P, Guess Trent M, Kia Mohammad
J Biomech Eng. 2013 Apr;135(4):041008. doi: 10.1115/1.4023982.
Detailed knowledge of knee joint kinematics and dynamic loading is essential for improving the design and outcomes of surgical procedures, tissue engineering applications, prosthetics design, and rehabilitation. The need for dynamic computational models that link kinematics, muscle and ligament forces, and joint contacts has long been recognized but such body-level forward dynamic models do not exist in recent literature. A main barrier in using computational models in the clinic is the validation of the in vivo contact, muscle, and ligament loads. The purpose of this study was to develop a full body, muscle driven dynamic model with subject specific leg geometries and validate it during squat and toe-rise motions. The model predicted loads were compared to in vivo measurements acquired with an instrumented knee implant. Data for this study were provided by the "Grand Challenge Competition to Predict In-Vivo Knee Loads" for the 2012 American Society of Mechanical Engineers Summer Bioengineering Conference. Data included implant and bone geometries, ground reaction forces, EMG, and the instrumented knee implant measurements. The subject specific model was developed in the multibody framework. The knee model included three ligament bundles for the lateral collateral ligament (LCL) and the medial collateral ligament (MCL), and one bundle for the posterior cruciate ligament (PCL). The implanted tibia tray was segmented into 326 hexahedral elements and deformable contacts were defined between the elements and the femoral component. The model also included 45 muscles on each leg. Muscle forces were computed for the muscle driven simulation by a feedback controller that used the error between the current muscle length in the forward simulation and the muscle length recorded during a kinematics driven inverse simulation. The predicted tibia forces and torques, ground reaction forces, electromyography (EMG) patterns, and kinematics were compared to the experimentally measured values to validate the model. Comparisons were done graphically and by calculating the mean average deviation (MAD) and root mean squared deviation (RMSD) for all outcomes. The MAD value for the tibia vertical force was 279 N for the squat motion and 325 N for the toe-rise motion, 45 N and 53 N for left and right foot ground reaction forces during the squat and 94 N and 82 N for toe-rise motion. The maximum MAD value for any of the kinematic outcomes was 7.5 deg for knee flexion-extension during the toe-rise motion.
详细了解膝关节运动学和动态负荷对于改进外科手术设计及效果、组织工程应用、假肢设计和康复至关重要。长期以来,人们一直认识到需要能够将运动学、肌肉和韧带力以及关节接触联系起来的动态计算模型,但近期文献中不存在此类全身水平的正向动态模型。在临床中使用计算模型的一个主要障碍是对体内接触、肌肉和韧带负荷进行验证。本研究的目的是开发一个具有个体特定腿部几何形状的全身肌肉驱动动态模型,并在深蹲和踮脚动作过程中对其进行验证。将模型预测的负荷与通过仪器化膝关节植入物获取的体内测量值进行比较。本研究的数据由2012年美国机械工程师协会夏季生物工程会议的“预测体内膝关节负荷大挑战竞赛”提供。数据包括植入物和骨骼几何形状、地面反作用力、肌电图(EMG)以及仪器化膝关节植入物测量值。在多体框架中开发了个体特定模型。膝关节模型包括外侧副韧带(LCL)和内侧副韧带(MCL)的三个韧带束,以及后交叉韧带(PCL)的一个束。将植入的胫骨托分割成326个六面体单元,并在单元与股骨部件之间定义了可变形接触。该模型每条腿还包括45块肌肉。通过反馈控制器计算肌肉驱动模拟的肌肉力,该反馈控制器利用正向模拟中当前肌肉长度与运动学驱动逆模拟中记录的肌肉长度之间的误差。将预测的胫骨干力量和扭矩、地面反作用力、肌电图(EMG)模式以及运动学与实验测量值进行比较,以验证模型。通过图形方式以及计算所有结果的平均平均偏差(MAD)和均方根偏差(RMSD)进行比较。深蹲动作时胫骨干垂直力的MAD值为279 N,踮脚动作时为325 N;深蹲时左右脚地面反作用力的MAD值分别为45 N和53 N,踮脚动作时为94 N和82 N。在踮脚动作过程中,任何运动学结果的最大MAD值为膝关节屈伸7.5度。