Inertial motion capture in conjunction with an artificial neural network can differentiate the gait patterns of hemiparetic stroke patients compared with able-bodied counterparts

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dc.contributor.authorScheffer C.
dc.contributor.authorCloete T.
dc.date.accessioned2012-06-06T07:59:01Z
dc.date.available2012-06-06T07:59:01Z
dc.date.issued2012
dc.description.abstractClinical gait analysis has proven to reduce uncertainties in selecting the appropriate quantity and type of treatment for patients with neuromuscular disorders. However, gait analysis as a clinical tool is under-utilised due to the limitations and cost of acquiring and managing data. To overcome these obstacles, inertial motion capture (IMC) recently emerged to counter the limitations attributed to other methods. This paper investigates the use of IMC for training and testing a backpropagation artificial neural network (ANN) for the purpose of distinguishing between hemiparetic stroke and able-bodied ambulation. Routine gait analysis was performed on 30 able-bodied control subjects and 28 hemiparetic stroke patients using an IMC system. An ANN was optimised to classify the two groups, achieving a repeatable network accuracy of 99.4%. It is concluded that an IMC system and appropriate computer methods may be useful for the planning and monitoring of gait rehabilitation therapy of stroke victims. © 2012 Taylor & Francis.
dc.identifier.citationComputer Methods in Biomechanics and Biomedical Engineering
dc.identifier.citation15
dc.identifier.citation3
dc.identifier.citation285
dc.identifier.citation294
dc.identifier.issn10255842
dc.identifier.otherdoi:10.1080/10255842.2010.527836
dc.identifier.urihttp://hdl.handle.net/10019.1/21266
dc.subjectGait analysis
dc.subjectHemiparetic stroke
dc.subjectInertial motion capture
dc.subjectNeural network
dc.titleInertial motion capture in conjunction with an artificial neural network can differentiate the gait patterns of hemiparetic stroke patients compared with able-bodied counterparts
dc.typeArticle
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