Optimal estimation of dynamically consistent kinematics and kinetics for forward dynamic simulation of gait.
Optimal estimation of dynamically consistent kinematics and kinetics for forward dynamic simulation of gait.
复制标题
用于步态正向动态模拟的动态一致运动学和动力学的最佳估计。
DOI：
10.1115/1.3005148
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Thelen,DarrylG
中科院分区：
文献类型：
--
作者：
Remy,CDavid;Thelen,DarrylG
正向动态模拟提供了一个强大的框架，用于表征内部负载和预测由于损伤，损伤或手术干预而引起的运动变化。然而，生成模拟的计算挑战极大地限制了用于模拟人类步态的前向动力学模型的使用和应用。在这项研究中，我们引入了一个最佳的估计方法，以有效地解决广义加速度，满足整体运动方程和最好的同意与测量的运动学和地面反作用力。对估计的加速度进行数值积分，以加强随时间的动态一致性，从而产生正向动态模拟。然后，使用数值优化来确定一组初始广义坐标和速度，其产生与步态的整个周期内的测量运动最一致的模拟。所提出的方法进行了评估与综合创建的运动学和力板数据，其中随机噪声和偏差误差的介绍。我们还将该方法应用于从五个年轻健康的成年人以首选速度行走的实验步态数据。我们表明，所提出的残余消除算法（REA）收敛于准确的解，减少了运动测量误差对关节力矩的不利影响，并消除了对标准逆动力学中出现的残余力的需要。近端观察到关节动力学的最大改善，该算法在髋关节处将标记噪声引起的关节力矩误差减少了20%以上，在下背部减少了50%以上。当使用REA从实验步态数据生成模拟时，模拟关节角度通常在记录值的范围内。因此，REA可以用作生成对象特定步态动力学的精确模拟的基础。
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 withinof 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.