Knee kinematics feature selection for surgical and nonsurgical arthroplasty candidate characterization

Amine Ben Arous, Michael Dunbar, Alexandre Fuentes, Glen Richardson, Neila Mezghani

Keywords: Knee Kinematic, Biomechanical data, Feature selection, Complexity measures, Arthroplasty

The purpose of this study is to investigate a method to select a set of knee kinematic data features to characterize surgical vs nonsurgical arthroplasty subjects. The kinematic features are generated from 3D knee kinematic data patterns, namely, rotations of flexion-extension, abduction-adduction, and tibial internal-external recorded during a walking task on a dedicated treadmill. The discrimination features are selected using three types of statistical complexity measures: the Fisher discriminant ratio, volume of overlap region, and feature efficiency. The interclass distance measurements which the features thus selected induce demonstrate their effectiveness to characterize surgical and nonsurgical subjects for arthroplasty.