Jensen Elisabeth, Lugade Vipul, Crenshaw Jeremy, Miller Emily, Kaufman Kenton
Mayo Graduate School, Biomedical Engineering and Physiology Track, Mayo Clinic, Rochester, MN 55905, USA; Motion Analysis Laboratory, Division of Orthopedic Research, Mayo Clinic, Charlton North L-110L, Rochester, MN 55905, USA.
Mayo Graduate School, Biomedical Engineering and Physiology Track, Mayo Clinic, Rochester, MN 55905, USA; Whitaker International Program, Chiang Mai University, Department of Physical Therapy, Chiang Mai 50200, Thailand.
J Biomech. 2016 Jun 14;49(9):1698-1704. doi: 10.1016/j.jbiomech.2016.03.046. Epub 2016 Apr 2.
Accurate and precise knee flexion axis identification is critical for prescribing and assessing tibial and femoral derotation osteotomies, but is highly prone to marker misplacement-induced error. The purpose of this study was to develop an efficient algorithm for post-hoc correction of the knee flexion axis and test its efficacy relative to other established algorithms. Gait data were collected on twelve healthy subjects using standard marker placement as well as intentionally misplaced lateral knee markers. The efficacy of the algorithm was assessed by quantifying the reduction in knee angle errors. Crosstalk error was quantified from the coefficient of determination (r(2)) between knee flexion and adduction angles. Mean rotation offset error (αo) was quantified from the knee and hip rotation kinematics across the gait cycle. The principal component analysis (PCA)-based algorithm significantly reduced r(2) (p<0.001) and caused αo,knee to converge toward 11.9±8.0° of external rotation, demonstrating improved certainty of the knee kinematics. The within-subject standard deviation of αo,hip between marker placements was reduced from 13.5±1.5° to 0.7±0.2° (p<0.001), demonstrating improved precision of the knee kinematics. The PCA-based algorithm performed at levels comparable to a knee abduction-adduction minimization algorithm (Baker et al., 1999) and better than a null space algorithm (Schwartz and Rozumalski, 2005) for this healthy subject population.
准确且精确地识别膝关节屈曲轴对于制定和评估胫骨与股骨旋转截骨术至关重要，但极易因标记点放置错误而产生误差。本研究的目的是开发一种用于膝关节屈曲轴事后校正的高效算法，并测试其相对于其他既定算法的有效性。使用标准标记点放置以及故意放置错误的膝关节外侧标记点，收集了12名健康受试者的步态数据。通过量化膝关节角度误差的减少来评估该算法的有效性。通过膝关节屈曲和内收角度之间的决定系数（r(2)）来量化串扰误差。通过整个步态周期中膝关节和髋关节的旋转运动学来量化平均旋转偏移误差（αo）。基于主成分分析（PCA）的算法显著降低了r(2)（p<0.001），并使αo,knee趋向于11.9±8.0°的外旋，表明膝关节运动学的确定性得到了改善。标记点放置之间αo,hip的受试者内标准差从13.5±1.5°降低到0.7±0.2°（p<0.001），表明膝关节运动学的精度得到了提高。对于该健康受试者群体，基于PCA的算法的表现与膝关节外展-内收最小化算法（Baker等人，1999年）相当，且优于零空间算法（Schwartz和Rozumalski，2005年）。
