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首页> 外文期刊>Journal of Biomechanics >A survey of formal methods for determining functional joint axes.
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A survey of formal methods for determining functional joint axes.

机译：确定功能性关节轴的正式方法的概述。

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Axes of rotation e.g. at the knee, are often generated from clinical gait analysis data to be used in the assessment of kinematic abnormalities, the diagnosis of disease, or the ongoing monitoring of a patient's condition. They are additionally used in musculoskeletal models to aid in the description of joint and segment kinematics for patient specific analyses. Currently available methods to describe joint axes from segment marker positions share the problem that when one segment is transformed into the coordinate system of another, artefacts associated with motion of the markers relative to the bone can become magnified. In an attempt to address this problem, a symmetrical axis of rotation approach (SARA) is presented here to determine a unique axis of rotation that can consider the movement of two dynamic body segments simultaneously, and then compared its performance in a survey against a number of previously proposed techniques. Using a generated virtual joint, with superimposed marker error conditions to represent skin movement artefacts, fitting methods (geometric axis fit, cylinder axis fit, algebraic axis fit) and transformation techniques (axis transformation technique, mean helical axis, Schwartz approach) were classified and compared with the SARA. Nearly all approaches were able to estimate the axis of rotation to within an RMS error of 0.1cm at large ranges of motion (90 degrees ). Although the geometric axis fit produced the least RMS error of approximately 1.2 cm at lower ranges of motion (5 degrees ) with a stationary axis, the SARA and Axis Transformation Technique outperformed all other approaches under the most demanding marker artefact conditions for all ranges of motion. The cylinder and algebraic axis fit approaches were unable to compute competitive AoR estimates. Whilst these initial results using the SARA are promising and are fast enough to be determined "on-line", the technique must now be proven in a clinical environment.
机译：旋转轴通常从临床步态分析数据生成膝盖处的数据，以用于运动异常评估，疾病诊断或对患者状况的持续监控。它们还用于肌肉骨骼模型，以帮助描述关节和节段运动学，以进行患者特定分析。当前可用的从片段标记器位置描述关节轴的方法存在一个问题，即当一个片段转换为另一片段的坐标系时，与标记器相对于骨骼的运动相关的伪影会被放大。为了解决这个问题，此处提出一种对称的旋转轴方法（SARA），以确定一个唯一的旋转轴，该旋转轴可以同时考虑两个动态主体段的运动，然后将其在调查中的性能与多个先前提出的技术。使用生成的虚拟关节，用叠加的标记错误条件表示皮肤运动伪影，对拟合方法（几何轴拟合，圆柱轴拟合，代数轴拟合）和转换技术（轴转换技术，平均螺旋轴，Schwartz方法）进行分类，与SARA相比。在大范围的运动（90度）下，几乎所有方法都能够将旋转轴估计为RMS误差在0.1cm以内。尽管几何轴拟合在固定轴的较低运动范围（5度）下产生的最小RMS误差约为1.2厘米，但在所有运动范围的最苛刻的标记伪影条件下，SARA和轴变换技术的性能均优于其他所有方法。圆柱和代数轴拟合方法无法计算竞争性AoR估计值。尽管使用SARA的这些初步结果令人鼓舞，并且足够快，可以“在线”确定，但该技术现在必须在临床环境中得到证明。

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