Guess Trent M, Stylianou Antonis P, Kia Mohammad
J Biomech Eng. 2014 Feb;136(2):021032. doi: 10.1115/1.4026359.
Detailed knowledge of knee kinematics and dynamic loading is essential for improving the design and outcomes of surgical procedures, tissue engineering applications, prosthetics design, and rehabilitation. This study used publicly available data provided by the "Grand Challenge Competition to Predict in-vivo Knee Loads" for the 2013 American Society of Mechanical Engineers Summer Bioengineering Conference (Fregly et al., 2012, "Grand Challenge Competition to Predict in vivo Knee Loads," J. Orthop. Res., 30, pp. 503-513) to develop a full body, musculoskeletal model with subject specific right leg geometries that can concurrently predict muscle forces, ligament forces, and knee and ground contact forces. The model includes representation of foot/floor interactions and predicted tibiofemoral joint loads were compared to measured tibial loads for two different cycles of treadmill gait. The model used anthropometric data (height and weight) to scale the joint center locations and mass properties of a generic model and then used subject bone geometries to more accurately position the hip and ankle. The musculoskeletal model included 44 muscles on the right leg, and subject specific geometries were used to create a 12 degrees-of-freedom anatomical right knee that included both patellofemoral and tibiofemoral articulations. Tibiofemoral motion was constrained by deformable contacts defined between the tibial insert and femoral component geometries and by ligaments. Patellofemoral motion was constrained by contact between the patellar button and femoral component geometries and the patellar tendon. Shoe geometries were added to the feet, and shoe motion was constrained by contact between three shoe segments per foot and the treadmill surface. Six-axis springs constrained motion between the feet and shoe segments. Experimental motion capture data provided input to an inverse kinematics stage, and the final forward dynamics simulations tracked joint angle errors for the left leg and upper body and tracked muscle length errors for the right leg. The one cycle RMS errors between the predicted and measured tibia contact were 178 N and 168 N for the medial and lateral sides for the first gait cycle and 209 N and 228 N for the medial and lateral sides for the faster second gait cycle. One cycle RMS errors between predicted and measured ground reaction forces were 12 N, 13 N, and 65 N in the anterior-posterior, medial-lateral, and vertical directions for the first gait cycle and 43 N, 15 N, and 96 N in the anterior-posterior, medial-lateral, and vertical directions for the second gait cycle.
深入了解膝关节运动学和动态负荷对于改善外科手术设计与效果、组织工程应用、假肢设计以及康复治疗至关重要。本研究使用了由“2013年美国机械工程师协会夏季生物工程会议体内膝关节负荷预测大挑战竞赛”提供的公开数据(Fregly等人,2012年,《体内膝关节负荷预测大挑战竞赛》,《矫形外科学研究杂志》,第30卷,第503 - 513页),来开发一个具有特定受试者右腿几何形状的全身肌肉骨骼模型,该模型能够同时预测肌肉力量、韧带力量以及膝关节和地面接触力。该模型包括足部/地面相互作用的表示,并且将预测的胫股关节负荷与两个不同跑步机步态周期的测量胫骨负荷进行了比较。该模型使用人体测量数据(身高和体重)来缩放通用模型的关节中心位置和质量属性,然后使用受试者的骨骼几何形状来更精确地定位髋部和脚踝。肌肉骨骼模型包括右腿上的44块肌肉,并且使用特定受试者的几何形状来创建一个具有12个自由度的解剖学右膝关节,该膝关节包括髌股关节和胫股关节。胫股关节运动受到胫骨假体与股骨部件几何形状之间定义的可变形接触以及韧带的约束。髌股关节运动受到髌骨按钮与股骨部件几何形状以及髌腱之间的接触约束。鞋的几何形状被添加到足部,并且鞋的运动受到每只脚的三个鞋段与跑步机表面之间的接触约束。六轴弹簧约束足部和鞋段之间的运动。实验性运动捕捉数据为逆运动学阶段提供输入,最终的正向动力学模拟跟踪左腿和上半身的关节角度误差以及右腿的肌肉长度误差。在第一个步态周期中,预测与测量的胫骨接触之间的单周期均方根误差在内侧和外侧分别为178 N和168 N,在较快的第二个步态周期中,内侧和外侧分别为209 N和228 N。在第一个步态周期中,预测与测量的地面反作用力之间的单周期均方根误差在前后、内外侧和垂直方向分别为12 N、13 N和65 N,在第二个步态周期中,前后、内外侧和垂直方向分别为43 N、15 N和96 N。