Optimizing Template Models to Quantifiably Assess Center of Mass Kinematic Reconstruction
Optimizing Template Models to Quantifiably Assess Center of Mass Kinematic Reconstruction
复制标题
优化模板模型以量化评估质量运动重建中心
DOI：
10.1109/icorr55369.2022.9896496
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Wensing, Patrick M.
中科院分区：
文献类型：
--
作者：
Kelly, David J.;Wensing, Patrick M.
质心在人体运动中起着至关重要的作用，但人体的冗余性增加了其动力学数学建模的复杂性。像双足弹簧加载倒立摆（B-SLIP）和虚拟支点（VPP）这样的模板模型通过消除冗余，同时保留所需的显著特性（如COM演化）来解决这种复杂性。然而，人类行走过程中COM的模板模型大多用于定性分析，存在COM垂直位移高估等问题。本文考虑了一种可量化的基于模板的人类步行分析方法，通过优化框架来设置模型参数值，以匹配显式和隐式考虑的步态特征。此外，研究表明，允许B-SLIP和VPP模型的腿部刚度在整个步态周期中变化，可以更好地匹配垂直COM轨迹，误差减少54%-63%。这些优化后的模板模型有望保留地面反作用力（GRF）信息，这在优化过程中没有明确考虑。未来的工作希望将这些优化的轨迹作为下肢膝关节-踝关节假体控制的参考。
The center of mass (COM) plays a fundamental role in human ambulation, but the redundant nature of the human body adds complexity to mathematically modeling its dynamics. Template models like the Bipedal Spring Loaded Inverted Pendulum (B-SLIP) and the Virtual Pivot Point (VPP) address this complexity by removing the redundancy while retaining desired salient characteristics, such as the COM evolution. However, template models for the COM during human walking have mostly been used for qualitative analysis due to issues such as overestimation of COM vertical displacement. This paper considers a quantifiable template-based analysis of human walking by using an optimization framework to set the model parameter values for matching both explicitly and implicitly considered gait characteristics. Furthermore, it is shown that allowing the leg stiffness of the B-SLIP and VPP model to vary throughout the gait cycle better matches vertical COM trajectories with 54%-63% error reduction. These optimized template models show promise in retaining ground reaction force (GRF) information, which is not explicitly considered during the optimization process. Future work looks to incorporate these optimized trajectories as a reference for control of a lower-limb knee-ankle prosthesis.
双足躯干-SLIP行走模型稳定控制策略
DOI：
10.1109/urai.2017.7992875
发表时间：
2017
期刊：
2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)
影响因子：
--
作者：
Minh Nhat Vu;Jongwoo Lee;Yonghwan Oh
通讯作者：
Yonghwan Oh
影响因子：
2
作者：
Lipfert, Susanne W.;Guenther, Michael;Seyfarth, Andre
通讯作者：
Seyfarth, Andre
影响因子：
2.4
作者：
Vielemeyer, Johanna;Mueller, Roy;Abel, Rainer
通讯作者：
Abel, Rainer