Machine learning-based classification of healthy and impaired gaits using 3D-GRF signals
Author | Nazmul Islam Shuzan, Md |
Author | Chowdhury, Muhammad E.H. |
Author | Bin Ibne Reaz, Mamun |
Author | Khandakar, Amith |
Author | Fuad Abir, Farhan |
Author | Ahasan Atick Faisal, Md. |
Author | Hamid Md Ali, Sawal |
Author | Bakar, Ahmad Ashrif A. |
Author | Hossain Chowdhury, Moajjem |
Author | Mahbub, Zaid B. |
Author | Monir Uddin, M. |
Author | Alhatou, Mohammed |
Available date | 2023-04-17T06:57:40Z |
Publication Date | 2023 |
Publication Name | Biomedical Signal Processing and Control |
Resource | Scopus |
Abstract | Gait analysis is helpful for rehabilitation, clinical diagnoses, and sporting activities. Among the gathered signals, ground reaction forces (GRF) may be used for assisting doctors in recognizing and categorizing gait patterns using Machine-Learning methods. In this study, GaitRec and Gutenberg databases were used, where GaitRec contains 2645 gait disorder (GD) patients and 211 Healthy Controls (HCs), and the Gutenberg database has 350 HCs. The combined database has HCs and four GD classes: hip, knee, ankle, and calcaneus. GD is an abnormality in the hip, knee, or ankle joints, whereas Calcaneus gait is calcaneus fractures or ankle fusions. We pre-processed the GRF signals, applied different feature extraction techniques, removed the highly correlated features, and ranked the features using three feature selection algorithms. K-nearest neighbour model (KNN) showed the top performance in terms of accuracy in all experiments. Four different experimental schemes were pursued: (i) 6 binary classifications; (ii) 1 three-class classification; (iii) 2 four-class classifications; (iv) one five-class classification. We also compared the performance of vertical GRF with three-dimensional GRF. We found that using three-dimensional GRF increased the overall performance. Furthermore, it is found that time-domain and Wavelet features are among the most useful in identifying gait patterns. The findings show promising performance in automated gait disorder classification. 2022 Elsevier Ltd |
Sponsor | This work was made possible by Qatar National Research Fund (QNRF) NPRP12S-0227-190164 and International Research Collaboration Co-Fund (IRCC) grant: IRCC-2021-001 and Universiti Kebangsaan Malaysia under Grant GUP-2021-019 and DPK-2021-001. The statements made herein are solely the responsibility of the authors. |
Language | en |
Publisher | Elsevier |
Subject | Feature extraction Feature ranking Gait classification Ground reaction force |
Type | Article |
Volume Number | 81 |
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