Abstract
Abstract
Objectives
Principal Component Analysis (PCA) is a useful method for analysing human motion data. The objective of this study was to use PCA to quantify the biggest variance in knee kinematics waveforms between a Non-Pathological (NP) group and individuals awaiting High Tibial Osteotomy (HTO) surgery.
Methods
Thirty knees (29 participants) who were scheduled for HTO surgery were included in this study. Twenty-eight NP volunteers were recruited into the study. Human motion analysis was performed during level gait using a modified Cleveland marker set. Subjects walked at their self-selected speed for a minimum of 6 successful trials. Knee kinematics were calculated within Visual3D (C-Motion). The first three Principal Components (PCs) of each input variable were selected. Single-component reconstruction was performed alongside representative extremes of each PC to aid interpretation of the biomechanical feature reconstructed by each component.
Results
Pre-operatively patient demographics included (age: 50.70 (8.71) years; height: 1.75 (.11) m; body mass: 90.57 (20.17) kg; mTFA: 7.75 (3.72) degrees varus; gait speed: 1.06 (0.23) m/s). The HTO cohort was significantly older and had a higher mass than the NP control participants. For knee kinematics the first three PCs explained 88%, 95% and 89% of the sagittal, frontal, and transverse planes, respectively. The main variances can be explained by sagittal plane magnitude differences, peak swing is associated with toe-off, a reduced knee flexion angle is associated with a longer time spent in stance, pre-HTO remain adducted during stance and pre-HTO patients remain more externally rotated during stance and latter part of swing.
Conclusions
This study has introduced PCA in trying to better understand the biomechanical differences between a control group and a cohort with medial knee osteoarthritis varus deformity awaiting HTO. Further analysis will be undertaken using PCA comparing pre- and post-surgery which will be of importance in clinical decision making.
