Reinbolt Jeffrey A, Haftka Raphael T, Chmielewski Terese L, Fregly Benjamin J
Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL 32611, USA.
IEEE Trans Biomed Eng. 2007 May;54(5):782-93. doi: 10.1109/TBME.2006.889187.
Variations in joint parameter (JP) values (axis positions and orientations in body segments) and inertial parameter (IP) values (segment masses, mass centers, and moments of inertia) as well as kinematic noise alter the results of inverse dynamics analyses of gait. Three-dimensional linkage models with joint constraints have been proposed as one way to minimize the effects of noisy kinematic data. Such models can also be used to perform gait optimizations to predict post-treatment function given pre-treatment gait data. This study evaluates whether accurate patient-specific JP and IP values are needed in three-dimensional linkage models to produce accurate inverse dynamics results for gait. The study was performed in two stages. First, we used optimization analyses to evaluate whether patient-specific JP and IP values can be calibrated accurately from noisy kinematic data, and second, we used Monte Carlo analyses to evaluate how errors in JP and IP values affect inverse dynamics calculations. Both stages were performed using a dynamic, 27 degrees-of-freedom, full-body linkage model and synthetic (i.e., computer generated) gait data corresponding to a nominal experimental gait motion. In general, JP but not IP values could be found accurately from noisy kinematic data. Root-mean-square (RMS) errors were 3 degrees and 4 mm for JP values and 1 kg, 22 mm, and 74 500 kg * mm2 for IP values. Furthermore, errors in JP but not IP values had a significant effect on calculated lower-extremity inverse dynamics joint torques. The worst RMS torque error averaged 4% bodyweight * height (BW * H) due to JP variations but less than 0.25% (BW * H) due to IP variations. These results suggest that inverse dynamics analyses of gait utilizing linkage models with joint constraints should calibrate the model's JP values to obtain accurate joint torques.
关节参数（JP）值（身体节段的轴位置和方向）、惯性参数（IP）值（节段质量、质心和转动惯量）的变化以及运动噪声会改变步态的逆动力学分析结果。提出了具有关节约束的三维连杆模型，作为一种最小化噪声运动学数据影响的方法。这种模型还可用于进行步态优化，以根据治疗前的步态数据预测治疗后的功能。本研究评估在三维连杆模型中是否需要准确的患者特异性JP和IP值，以产生准确的步态逆动力学结果。该研究分两个阶段进行。首先，我们使用优化分析来评估是否可以从噪声运动学数据中准确校准患者特异性JP和IP值，其次，我们使用蒙特卡罗分析来评估JP和IP值的误差如何影响逆动力学计算。两个阶段均使用一个动态的、27自由度的全身连杆模型以及与名义实验步态运动对应的合成（即计算机生成）步态数据进行。一般来说，可以从噪声运动学数据中准确找到JP值，但找不到IP值。JP值的均方根（RMS）误差为3度和4毫米，IP值的均方根误差为1千克、22毫米和74500千克·平方毫米。此外，JP值的误差而非IP值的误差对计算出的下肢逆动力学关节扭矩有显著影响。由于JP变化导致的最差RMS扭矩误差平均为4%体重×身高（BW×H），但由于IP变化导致的误差小于0.25%（BW×H）。这些结果表明，利用具有关节约束的连杆模型进行步态的逆动力学分析时，应校准模型的JP值以获得准确的关节扭矩。
