In this paper, a gait patterns classification system is proposed, which is based on Mahalanobis–Taguchi System (MTS). The classification of gait patterns is necessary in order to ascertain the rehab outcome among anterior cruciate ligament reconstruction (ACLR) patients. (1) Background: One of the most critical discussion about when ACLR patients should return to work (RTW). The objective was to use Mahalanobis distance (MD) to classify between the gait patterns of the control and ACLR groups, while the Taguchi Method (TM) was employed to choose the useful features. Moreover, MD was also utilised to ascertain whether the ACLR group approaching RTW. The combination of these two methods is called as Mahalanobis-Taguchi System (MTS). (2) Methods: This study compared the gait of 15 control subjects to a group of 10 subjects with laboratory. Later, the data were analysed using MTS. The analysis was based on 11 spatiotemporal parameters. (3) Results: The results showed that gait deviations can be identified successfully, while the ACLR can be classified with higher precision by MTS. The MDs of the healthy group ranged from 0.560 to 1.180, while the MDs of the ACLR group ranged from 2.308 to 1509.811. Out of the 11 spatiotemporal parameters analysed, only eight parameters were considered as useful features. (4) Conclusions: These results indicate that MTS can effectively detect the ACLR recovery progress with reduced number of useful features. MTS enabled doctors or physiotherapists to provide a clinical assessment of their patients with more objective way.
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.
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