Department of Mechanical Engineering & Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Av. Diagonal, 647 08028 Barcelona, Spain.
School of Medical Sciences and Health of Juiz de Fora, SUPREMA, Alameda Salvaterra, 200 - Salvaterra, 36033-003, Juiz de Fora, MG, Brazil.
Gait Posture. 2019 Sep;73:116-119. doi: 10.1016/j.gaitpost.2019.07.191. Epub 2019 Jul 13.
Experimental and modeling errors can lead to dynamically inconsistent results when performing inverse dynamic analyses of human movement. Adding low-value residual pelvis actuators could deal with such a problem. However, in certain tasks, these residuals may remain quite large, and strategies based on motion or force variation must be applied.
Can the dynamic inconsistency be handled by an optimal control algorithm that changes the measured kinematics in the preparatory phase of the single leg triple hop test, a relatively high-speed and torque-demanding task, so that residuals are kept within a low range?
The proposed optimal control algorithm was developed as a tracking problem, in which the implicit form of dynamics was used. Equations of motion were introduced as path constraints, as well as residual forces and moments acting on the pelvis. To do so, GPOPS-II and IPOPT were employed to solve the optimization problem. Furthermore, OpenSim API was called at each iteration to solve the equations of motion through an inverse dynamic analysis.
Results presented a high reduction in all six components of residual actuators during the entire task. Moreover, resulting motion after the optimization showed a very similar evolution than the reference motion before the ascending phase of the task. Once the ascending phase started, some coordinates presented a more significant discrepancy compared to the reference, such as the pelvis tilt and lumbar extension.
The findings suggest that the proposed algorithm can deal with dynamic inconsistency in high-speed tasks, obtaining low residual forces and moments while keeping similar kinematics. Hence, it could complement other optimal control algorithms that simulate new motions, relying on dynamically consistent data.
在进行人体运动的逆动力学分析时，实验和建模误差可能导致动态不一致的结果。添加低值残余骨盆驱动器可以解决这个问题。然而，在某些任务中，这些残余值可能仍然很大，必须应用基于运动或力变化的策略。
在单腿三级跳测试的准备阶段，一种相对高速和高扭矩要求的任务，可以通过改变测量运动学的最优控制算法来处理动态不一致问题，使残余值保持在较低范围内吗？
所提出的最优控制算法是作为一个跟踪问题开发的，其中使用了动力学的隐式形式。运动方程被引入作为路径约束，以及作用在骨盆上的残余力和力矩。为此，使用 GPOPS-II 和 IPOPT 来解决优化问题。此外，在每次迭代时调用 OpenSim API 通过逆动力学分析来求解运动方程。
结果表明，在整个任务过程中，所有六个残余驱动器组件的残余值都有很大的减少。此外，优化后的运动结果与任务上升阶段之前的参考运动非常相似。一旦上升阶段开始，一些坐标与参考值相比就会出现较大的差异，例如骨盆倾斜和腰椎伸展。
研究结果表明，所提出的算法可以处理高速任务中的动态不一致问题，同时获得低残余力和力矩，保持相似的运动学。因此，它可以补充其他依赖于动态一致数据模拟新运动的最优控制算法。
