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Open AccessArticle

Gait Classification Using Mahalanobis–Taguchi System for Health Monitoring Systems Following Anterior Cruciate Ligament Reconstruction

1
School of Mechatronic Engineering, Universiti Malaysia Perlis, Pauh Putra Campus, Arau 02600, Perlis, Malaysia
2
Centre for Applied Biomechanics, Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
3
The Chancellery, University of Malaysia Terengganu, Kuala Terengganu 21030, Malaysia
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(16), 3306; https://doi.org/10.3390/app9163306
Received: 22 July 2019 / Revised: 5 August 2019 / Accepted: 8 August 2019 / Published: 12 August 2019
(This article belongs to the Section Applied Biosciences and Bioengineering)
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Abstract

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. View Full-Text
Keywords: Mahalanobis–Taguchi system; ACLR; return to work; spatiotemporal; gait classification Mahalanobis–Taguchi system; ACLR; return to work; spatiotemporal; gait classification
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Sakeran, H.; Abu Osman, N.A.; Abdul Majid, M.S. Gait Classification Using Mahalanobis–Taguchi System for Health Monitoring Systems Following Anterior Cruciate Ligament Reconstruction. Appl. Sci. 2019, 9, 3306.

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