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AuthorFaisal, Md Ahasan Atick
AuthorChowdhury, Muhammad E.H.
AuthorMahbub, Zaid Bin
AuthorPedersen, Shona
AuthorAhmed, Mosabber Uddin
AuthorKhandakar, Amith
AuthorAlhatou, Mohammed
AuthorNabil, Mohammad
AuthorAra, Iffat
AuthorBhuiyan, Enamul Haque
AuthorMahmud, Sakib
AuthorAbdulMoniem, Mohammed
Available date2023-05-02T10:30:34Z
Publication Date2023-03-27
Publication NameApplied Intelligence
Identifierhttp://dx.doi.org/10.1007/s10489-023-04557-w
CitationFaisal, 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.
ISSN0924-669X
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85150954938&origin=inward
URIhttp://hdl.handle.net/10576/42213
AbstractNeurodegenerative 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.
SponsorThis 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.
Languageen
PublisherSpringer Nature
SubjectDeep learning
Feature extraction
Gait analysis
Ground reaction force
Neurodegenerative diseases
TitleNDDNet: a deep learning model for predicting neurodegenerative diseases from gait pattern
TypeArticle
Pagination1-13
ESSN1573-7497
dc.accessType Full Text


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