Knee kinematic signals clustering for the Identification of sagittal and transverse gait patterns

TLDR: The purpose of this study is to investigate knee kinematic signals clustering by principal component analysis to identify meaningful patterns in normal gait knee flexion/extension and tibial internal/external rotation signals.

Abstract: The purpose of this study is to investigate knee kinematic signals clustering by principal component analysis. The aim is to identify meaningful patterns in normal gait knee flexion/extension and tibial internal/external rotation signals. To preserve all of the information contained in these kinematics signals, the analysis uses the entire angle curve over a gait cycle rather then a few features extracted from this curve as done traditionally. To reduce processing complexity, the data dimensionality is reduced without loss of relevant information by projecting the gait curve onto a subspace of significant principal components (PCs). Gait patterns are then extracted by a discriminant analysis of the set of training data based on the PCs sign. The analysis identified two representation patterns for each of the flexion/extension (sagital plane) and the tibial internal/external rotation (transverse plane). These patterns were validated both by the clustering silhouette width and clinical interpretation.