Remy C David, Thelen Darryl G
Department of Mechanical Engineering, University of Wisconsin-Madison, 1513 University Avenue, Madison, WI 53706, USA.
J Biomech Eng. 2009 Mar;131(3):031005. doi: 10.1115/1.3005148.
Forward dynamic simulation provides a powerful framework for characterizing internal loads and for predicting changes in movement due to injury, impairment or surgical intervention. However, the computational challenge of generating simulations has greatly limited the use and application of forward dynamic models for simulating human gait. In this study, we introduce an optimal estimation approach to efficiently solve for generalized accelerations that satisfy the overall equations of motion and best agree with measured kinematics and ground reaction forces. The estimated accelerations are numerically integrated to enforce dynamic consistency over time, resulting in a forward dynamic simulation. Numerical optimization is then used to determine a set of initial generalized coordinates and speeds that produce a simulation that is most consistent with the measured motion over a full cycle of gait. The proposed method was evaluated with synthetically created kinematics and force plate data in which both random noise and bias errors were introduced. We also applied the method to experimental gait data collected from five young healthy adults walking at a preferred speed. We show that the proposed residual elimination algorithm (REA) converges to an accurate solution, reduces the detrimental effects of kinematic measurement errors on joint moments, and eliminates the need for residual forces that arise in standard inverse dynamics. The greatest improvements in joint kinetics were observed proximally, with the algorithm reducing joint moment errors due to marker noise by over 20% at the hip and over 50% at the low back. Simulated joint angles were generally within 1 deg of recorded values when REA was used to generate a simulation from experimental gait data. REA can thus be used as a basis for generating accurate simulations of subject-specific gait dynamics.
正向动力学仿真为表征内部负荷以及预测因损伤、功能障碍或手术干预导致的运动变化提供了一个强大的框架。然而，生成仿真的计算挑战极大地限制了正向动力学模型在模拟人类步态方面的使用和应用。在本研究中，我们引入了一种最优估计方法，以有效地求解满足整体运动方程且与测量的运动学和地面反作用力最佳吻合的广义加速度。对估计的加速度进行数值积分，以确保随时间的动态一致性，从而得到正向动力学仿真。然后使用数值优化来确定一组初始广义坐标和速度，这些坐标和速度能生成在整个步态周期内与测量运动最一致的仿真。我们使用合成创建的运动学和测力板数据对所提出的方法进行了评估，其中引入了随机噪声和偏差误差。我们还将该方法应用于从五名年轻健康成年人以偏好速度行走时收集的实验步态数据。我们表明，所提出的残差消除算法（REA）收敛到精确解，减少了运动学测量误差对关节力矩的有害影响，并且消除了标准逆动力学中出现的残余力的需求。在近端观察到关节动力学的最大改善，该算法将由于标记噪声导致的髋关节力矩误差降低了20%以上，下背部降低了50%以上。当使用REA从实验步态数据生成仿真时，模拟的关节角度通常在记录值的1度范围内。因此，REA可作为生成特定个体步态动力学精确仿真的基础。
