Identification of passive elastic joint moment-angle relationships in the lower extremity
Identification of passive elastic joint moment-angle relationships in the lower extremity
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DOI：
10.1016/j.jbiomech.2006.12.017
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发表时间：
2007-01-01
影响因子：
2.4
通讯作者：
Thelen, Darryl G.
中科院分区：
文献类型：
--
作者：
Silder, Amy;Whittington, Ben;Thelen, Darryl G.
本研究的目的是开发一种方法，用于确定特定的被动弹性关节力矩-角度关系的下肢，随后可用于估计被动贡献的关节动力学在步态。20名健康的年轻人参加了这项研究。受试者侧躺，通过低摩擦推车将其优势肢体支撑在桌子上。物理治疗师使用两个手持式3D测力传感器通过全矢状髋关节、膝关节和踝关节的运动范围缓慢地操纵肢体。使用被动标记运动捕获系统测量的下肢运动学和测力传感器读数来计算关节角度和相关的被动关节力矩。我们制定了一个被动关节力矩角模型，其中包括八个指数函数，以解释通过被动拉伸单关节结构和双关节肌肉产生的力。通过最小化模型预测和实验测量时刻之间的平方误差之和来估计个体受试者的模型参数。该模型的预测密切复制测量关节力矩的平均均方根误差为2.5，1.4和0.7 Nm的髋关节，膝关节和踝关节分别。我们表明，该模型可以与步态运动学估计被动关节力矩在步行过程中。被动髋关节的时刻是大量的从终端的立场，通过最初的摆动，与能量被存储为髋关节延长，随后返回在前和最初的摆动。我们的结论是，所提出的方法可以提供定量的见解，被动机制在正常和异常步态中发挥的潜在重要作用。(c)2007爱思唯尔有限公司保留所有权利。
The purpose of this study was to develop a method for identifying subject-specific passive elastic joint moment-angle relationships in the lower extremity, which could subsequently be used to estimate passive contributions to joint kinetics during gait. Twenty healthy young adults participated in the study. Subjects were positioned side-lying with their dominant limb supported on a table via low-friction carts. A physical therapist slowly manipulated the limb through full sagittal hip, knee, and ankle ranges of motion using two hand-held 3D load cells. Lower extremity kinematics, measured with a passive marker motion capture system, and load cell readings were used to compute joint angles and associated passive joint moments. We formulated a passive joint moment-angle model that included eight exponential functions to account for forces generated via the passive stretch of uni-articular structures and bi-articular muscles. Model parameters were estimated for individual subjects by minimizing the sum of squared errors between model predicted and experimentally measured moments. The model predictions closely replicated measured joint moments with average root-mean-squared errors of 2.5, 1.4, and 0.7 Nm about the hip, knee, and ankle respectively. We show that the models can be coupled with gait kinematics to estimate passive joint moments during walking. Passive hip moments were substantial from terminal stance through initial swing, with energy being stored as the hip extended and subsequently returned during pre- and initial swing. We conclude that the proposed methodology could provide quantitative insights into the potentially important role that passive mechanisms play in both normal and abnormal gait. (c) 2007 Elsevier Ltd. All rights reserved.