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المؤلفNazmul Islam Shuzan, Md
المؤلفChowdhury, Muhammad E.H.
المؤلفBin Ibne Reaz, Mamun
المؤلفKhandakar, Amith
المؤلفFuad Abir, Farhan
المؤلفAhasan Atick Faisal, Md.
المؤلفHamid Md Ali, Sawal
المؤلفBakar, Ahmad Ashrif A.
المؤلفHossain Chowdhury, Moajjem
المؤلفMahbub, Zaid B.
المؤلفMonir Uddin, M.
المؤلفAlhatou, Mohammed
تاريخ الإتاحة2023-04-17T06:57:40Z
تاريخ النشر2023
اسم المنشورBiomedical Signal Processing and Control
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1016/j.bspc.2022.104448
معرّف المصادر الموحدhttp://hdl.handle.net/10576/41923
الملخصGait 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
راعي المشروعThis 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.
اللغةen
الناشرElsevier
الموضوعFeature extraction
Feature ranking
Gait classification
Ground reaction force
العنوانMachine learning-based classification of healthy and impaired gaits using 3D-GRF signals
النوعArticle
رقم المجلد81
dc.accessType Abstract Only


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