Representative Knee Kinematic Patterns Identification Using Within-Subject Variability Analysis

The purpose of this study is to perform data pre-processing to identify, for a subject, robust representative knee kinematic patterns from a set of three-dimensional (3D) curves, recorded during gait. In this study, we identify a representative gait of a given subject using the within-subject variability evaluation for outliers' removal and reliable curves' selection. The proposed pre-processing method encompasses steps of gait events detection, normalization, outlier detection and cycles' selection. In order to measure the reliability of the subjects' curves before and after pre-processing, we computed the intra-class correlation (ICC) estimates and their 95% confidence intervals for knee kinematics of a multicentric dataset of 226 asymptomatic subjects and knee osteoarthrosis (OA). The experimental results show that the proposed pre-processing method allows to identify representative knee kinematic patterns.