Bennett Hunter J, Fleenor Kristina, Weinhandl Joshua T
Department of Human Movement Sciences, Old Dominion University, 2016 Student Recreation Center, Norfolk, VA 23529, United States.
Department of Kinesiology, Recreation, and Sport Studies, The University of Tennessee, 322 HPER Building, 1914 Andy Holt Avenue, Knoxville, TN 37996-2700, United States.
J Biomech. 2018 Nov 16;81:122-131. doi: 10.1016/j.jbiomech.2018.10.003. Epub 2018 Oct 14.
Many methodologies exist to predict the hip joint center (HJC), of which regression based on anatomical landmarks appear most common. Despite the fact that predicted HJC locations vary depending upon chosen method, inter-study comparisons and inferences about populations are commonly made. The purpose of this study was to create a normative database of hip and knee biomechanics during walking, running, and single leg landings based on five commonly utilized HJC methods to serve as a reference for inter-study comparisons. Secondarily, we devised to provide comparisons of peak knee angles and hip angles, moments, and powers from the five HJC methods. Thirty healthy young adults performed walking, running, and single leg landing tasks at self-selected speeds (walking/running) and at 90% of their maximum jump height (landing). Three-dimensional motion capture and ground reaction forces were collected during all tasks. Five different HJC prediction methods: Bell, Davis, Hara, Harrington, and Greater Trochanter were implemented separately in a 6 degree of freedom model. Predicted HJC locations, direct kinematics, and inverse dynamics were computed for all tasks. Predicted HJC mediolateral, anteroposterior, and superior-inferior locations differed between methods by an average of 1.3, 2.9, and 1.4 cm, respectively. A database was created using the mean of all subjects for all five methods. In addition, one-way ANOVAs were used to compare triplanar peak angles, moments, and powers between the methods. The database of hip and knee biomechanics illustrates (1) variability between methods increases with more dynamic tasks (running/landing vs. walking) and (2) frontal and transverse plane hip and knee biomechanics are more variable between methods. Comparisons between methods found 38 and 16 main effect differences in hip and knee biomechanics, respectively. The Greater Trochanter method provided the most differences compared with other methods, while the Davis method provided the least differences. The database constructed provides an important reference for inter-study comparisons and details the impact of anatomical regression methods for predicting the HJC.
存在多种预测髋关节中心（HJC）的方法，其中基于解剖标志点的回归方法似乎最为常见。尽管预测的HJC位置因所选方法而异，但不同研究之间的比较以及对人群的推断却很常见。本研究的目的是基于五种常用的HJC方法创建一个关于步行、跑步和单腿着地时髋膝关节生物力学的标准数据库，作为不同研究之间比较的参考。其次，我们打算对五种HJC方法的膝关节和髋关节峰值角度、力矩和功率进行比较。30名健康的年轻成年人以自我选择的速度（步行/跑步）以及其最大跳跃高度的90%（着地）进行步行、跑步和单腿着地任务。在所有任务过程中收集三维运动捕捉数据和地面反作用力。在一个六自由度模型中分别实施五种不同的HJC预测方法：贝尔（Bell）、戴维斯（Davis）、原田（Hara）、哈灵顿（Harrington）和大转子（Greater Trochanter）。计算所有任务的预测HJC位置、直接运动学和逆动力学。不同方法预测的HJC内外侧、前后和上下位置平均分别相差1.3厘米、2.9厘米和1.4厘米。使用所有五种方法中所有受试者的平均值创建了一个数据库。此外，使用单因素方差分析来比较不同方法之间的三平面峰值角度、力矩和功率。髋膝关节生物力学数据库表明：（1）不同方法之间的变异性随着任务动态性增强（跑步/着地与步行相比）而增加；（2）不同方法之间，额状面和横断面的髋膝关节生物力学变异性更大。不同方法之间的比较发现，髋膝关节生物力学分别有38个和16个主要效应差异。与其他方法相比，大转子方法的差异最多，而戴维斯方法的差异最少。构建的数据库为不同研究之间的比较提供了重要参考，并详细说明了解剖回归方法对预测HJC的影响。
