Gait Analysis and Mathematical Index-Based Health Management Following Anterior Cruciate Ligament Reconstruction

Abstract

Gait analysis is recognized as a method used in quantifying gait disorders and in clinical evaluations of patients. However, the current guidelines for the evaluation of post anterior cruciate ligament reconstruction (ACLR) patient outcomes are primarily based on qualitative assessments. This study aims to apply gait analyses and mathematical, index-based health management, using the Mahalanobis Taguchi System (MTS) and the Kanri Distance Calculator (KDC) to diagnose the level of the gait abnormality and to identify its contributing factors following ACLR. It is hypothesized that (1) the method is able to discriminate the gait patterns between a healthy group (HG) and patients with ACLR (PG), and (2) several contributing factors may affect ACLR patients’ rehabilitation performance. This study compared the gait of 10 subjects in the PG group with 15 subjects in the HG. The analysis was based on 11 spatiotemporal parameters. Gait data of all subjects were collected in a motion analysis laboratory. The data were then analyzed using MTS and KDC. In this study, two significant groups were recognized: the HG, who achieved results which were within the Mahalanobis space (MS), and (ii) the PG who achieved results above the MS. The results may be seen as being on-target and off-target, respectively. Based on the analysis, three variables (i.e., step width, single support time, and double support time) affected patient performance and resulted in an average mark of above 1.5 Mahalanobis distance (MD). The results indicated that by focusing on the contributing factors that affect the rehabilitation performance of the patients, it is possible to provide individualized and need-based treatment.