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dc.contributor.authorSharif Bidabadi, Shiva
dc.contributor.authorMurray, Iain
dc.contributor.authorLee, GYF
dc.contributor.authorMorris, Susan
dc.contributor.authorTan, Tele
dc.identifier.citationSharif Bidabadi, S. and Murray, I. and Lee, G.Y.F. and Morris, S. and Tan, T. 2019. Classification of foot drop gait characteristic due to lumbar radiculopathy using machine learning algorithms. Gait and Posture. 71: pp. 234-240.

© 2019 Elsevier B.V. Background: Recently, the study of walking gait has received significant attention due to the importance of identifying disorders relating to gait patterns. Characterisation and classification of different common gait disorders such as foot drop in an effective and accurate manner can lead to improved diagnosis, prognosis assessment, and treatment. However, currently visual inspection is the main clinical method to evaluate gait disorders, which is reliant on the subjectivity of the observer, leading to inaccuracies. Research question: This study examines if it is feasible to use commercial off-the-shelf Inertial measurement unit sensors and supervised learning methods to distinguish foot drop gait disorder from the normal walking gait pattern. Method: The gait data collected from 56 adults diagnosed with foot drop due to L5 lumbar radiculopathy (with MRI verified compressive pathology), and 30 adults with normal gait during multiple walking trials on a flat surface. Machine learning algorithms were applied to the inertial sensor data to investigate the feasibility of classifying foot drop disorder. Results: The best three performing results were 88.45%, 86.87% and 86.08% accuracy derived from the Random Forest, SVM, and Naive Bayes classifiers respectively. After applying the wrapper feature selection technique, the top performance was from the Random Forest classifier with an overall accuracy of 93.18%. Significance: It is demonstrated that the combination of inertial sensors and machine learning algorithms, provides a promising and feasible solution to differentiating L5 radiculopathy related foot drop from normal walking gait patterns. The implication of this finding is to provide an objective method to help clinical decision making.

dc.subjectFoot drop
dc.subjectGait classification
dc.subjectInertial measurement unit
dc.subjectMachine learning
dc.titleClassification of foot drop gait characteristic due to lumbar radiculopathy using machine learning algorithms
dc.typeJournal Article
dcterms.source.titleGait and Posture
curtin.departmentSchool of Civil and Mechanical Engineering
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Sciences
curtin.departmentSchool of Physiotherapy and Exercise Science
curtin.accessStatusFulltext not available
curtin.facultyFaculty of Science and Engineering
curtin.facultyFaculty of Health Sciences
curtin.contributor.orcidMurray, Iain [0000-0003-1840-9624]
curtin.contributor.researcheridMurray, Iain [B-8795-2013]
curtin.contributor.scopusauthoridSharif Bidabadi, S [57195989731]
curtin.contributor.scopusauthoridLee, GYF [7404851391]
curtin.contributor.scopusauthoridTan, T [57207189507]
curtin.contributor.scopusauthoridMurray, Iain [55605780042]
curtin.contributor.scopusauthoridMorris, Susan [24171577300]

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