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المؤلفFaisal, Md Ahasan Atick
المؤلفChowdhury, Muhammad E.H.
المؤلفMahbub, Zaid Bin
المؤلفPedersen, Shona
المؤلفAhmed, Mosabber Uddin
المؤلفKhandakar, Amith
المؤلفAlhatou, Mohammed
المؤلفNabil, Mohammad
المؤلفAra, Iffat
المؤلفBhuiyan, Enamul Haque
المؤلفMahmud, Sakib
المؤلفAbdulMoniem, Mohammed
تاريخ الإتاحة2023-05-02T10:30:34Z
تاريخ النشر2023-03-27
اسم المنشورApplied Intelligence
المعرّفhttp://dx.doi.org/10.1007/s10489-023-04557-w
الاقتباسFaisal, M. A. A., Chowdhury, M. E., Mahbub, Z. B., Pedersen, S., Ahmed, M. U., Khandakar, A., ... & AbdulMoniem, M. (2023). NDDNet: a deep learning model for predicting neurodegenerative diseases from gait pattern. Applied Intelligence, 1-13.
الرقم المعياري الدولي للكتاب0924-669X
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85150954938&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/42213
الملخصNeurodegenerative diseases damage neuromuscular tissues and deteriorate motor neurons which affects the motor capacity of the patient. Particularly the walking gait is greatly influenced by the deterioration process. Early detection of anomalous gait patterns caused by neurodegenerative diseases can help the patient to prevent associated risks. Previous studies in this domain relied on either features extracted from gait parameters or the Ground Reaction Force (GRF) signal. In this work, we aim to combine both GRF signals and extracted features to provide a better analysis of walking gait patterns. For this, we designed NDDNet, a novel neural network architecture to process both of these data simultaneously to detect 3 different Neurodegenerative Diseases (NDDs). We have done several experiments on the data collected from 64 participants and got 96.75% accuracy on average in detecting 3 types of NDDs. The proposed method might provide a way to get the most out of the data in hand while working with GRF signals and help diagnose patients with an anomalous gait more effectively.
راعي المشروعThis work was supported in part by the Qatar National Research Fund under Grant NPRP12S-0227-190164 and in part by the International Research Collaboration Co-Fund (IRCC) through Qatar University under Grant IRCC-2021- 001. The statements made herein are solely the responsibility of the authors. This open-access publication is supported by Qatar National Library.
اللغةen
الناشرSpringer Nature
الموضوعDeep learning
Feature extraction
Gait analysis
Ground reaction force
Neurodegenerative diseases
العنوانNDDNet: a deep learning model for predicting neurodegenerative diseases from gait pattern
النوعArticle
الصفحات1-13
ESSN1573-7497
dc.accessType Full Text


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