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dc.contributor.authorBidabadi, Shiva Sharif
dc.contributor.authorTan, Tele
dc.contributor.authorMurray, Iain
dc.contributor.authorLee, G.
dc.date.accessioned2019-09-17T02:28:09Z
dc.date.available2019-09-17T02:28:09Z
dc.date.issued2019
dc.identifier.citationBidabadi, S.S. and Tan, T. and Murray, I. and Lee, G. 2019. Tracking foot drop recovery following lumbar-spine surgery, applying multiclass gait classification using machine learning techniques. Sensors. 19 (11): ARTN 2542.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/76314
dc.identifier.doi10.3390/s19112542
dc.description.abstract

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. The ability to accurately perform human gait evaluation is critical for orthopedic foot and ankle surgeons in tracking the recovery process of their patients. The assessment of gait in an objective and accurate manner can lead to improvement in diagnoses, treatments, and recovery. Currently, visual inspection is the most common clinical method for evaluating the gait, but this method can be subjective and inaccurate. The aim of this study is to evaluate the foot drop condition in an accurate and clinically applicable manner. The gait data were collected from 56 patients suffering from foot drop with L5 origin gathered via a system based on inertial measurement unit sensors at different stages of surgical treatment. Various machine learning (ML) algorithms were applied to categorize the data into specific groups associated with the recovery stages. The results revealed that the random forest algorithm performed best out of the selected ML algorithms, with an overall 84.89% classification accuracy and 0.3785 mean absolute error for regression.

dc.languageEnglish
dc.publisherMDPI
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectScience & Technology
dc.subjectPhysical Sciences
dc.subjectTechnology
dc.subjectChemistry, Analytical
dc.subjectElectrochemistry
dc.subjectInstruments & Instrumentation
dc.subjectChemistry
dc.subjectfoot drop
dc.subjectgait classification
dc.subjectmachine learning
dc.subjectinertial measurement unit
dc.subjectFUNCTIONAL AMBULATION
dc.subjectVALIDATION
dc.subjectSTRENGTH
dc.subjectVALIDITY
dc.titleTracking foot drop recovery following lumbar-spine surgery, applying multiclass gait classification using machine learning techniques
dc.typeJournal Article
dcterms.source.volume19
dcterms.source.number11
dcterms.source.issn1424-8220
dcterms.source.titleSensors
dc.date.updated2019-09-17T02:27:44Z
curtin.departmentSchool of Civil and Mechanical Engineering
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Sciences
curtin.accessStatusOpen access
curtin.facultyFaculty of Science and Engineering
curtin.contributor.orcidMurray, Iain [0000-0003-1840-9624]
curtin.contributor.researcheridMurray, Iain [B-8795-2013]
curtin.identifier.article-numberARTN 2542
dcterms.source.eissn1424-8220
curtin.contributor.scopusauthoridTan, Tele [7402022415]
curtin.contributor.scopusauthoridMurray, Iain [55605780042]


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