A principal component analysis approach to correcting the knee flexion axis during gait

Abstract

© 2016 Elsevier Ltd. 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 (r2) 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 r2(p<0.001) and caused αo,kneeto converge toward 11.9±8.0° of external rotation, demonstrating improved certainty of the knee kinematics. The within-subject standard deviation of αo,hipbetween 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.