Kinematic Data Clustering for Healthy Knee Gait Characterization

Abstract

The purpose of this study is to investigate data clustering to determine representative patterns in three-dimensional (3D) knee kinematic data measurements. Kinematic data are high-dimensional vectors to describe the temporal variations of the three fundamental angles of knee rotation during a walking cycle, namely the abduction/adduction angle, with respect to the frontal plane, the flexion/extension angle, with respect to the sagittal plane, and internal/external angle, with respect to the transverse plane. To offset the curse of dimensionality, inherent to high dimensional data pattern analysis, the method reduces dimensionality by isometric mapping without affecting information content. The data thus simplified is then clustered by the DBSCAN algorithm. The method has been tested on a large database of 165 healthy knee kinematic data measurements. Clusters are validated in terms of the silhouette index, the Dunn index, and connectivity. Results show that a two-cluster characterization of the kinematic knee data in each plane is quite effective. A further clinical investigation shows that the men and women knee patterns are balanced between the two clusters and, for 80% of participants, the right and left knees are in the same cluster.