Show simple item record

dc.contributor.advisorHewson, David, John
dc.contributor.advisorTaylor, Denise
dc.contributor.advisorMcNair, Peter, John
dc.contributor.authorBassement, Jennifer
dc.date.accessioned2015-06-08T00:05:34Z
dc.date.available2015-06-08T00:05:34Z
dc.date.copyright2014
dc.date.created2015
dc.identifier.urihttp://hdl.handle.net/10292/8839
dc.description.abstractFalls concern a third of the people aged over 65y and lead to the loss of functional ability. The detection of risks factors of falls is essential for early interven- tion. Six intrinsic risk factors of fall: vision, vestibular system, joint range of motion, leg muscle strength, joint proprioception and foot cutaneous propriocep- tion were assessed with clinical tests before and after temporarily degradation. Standing balance was recorded on a force plate. From the force plate, 198 parameters of the centre of pressure displacement were computed. The parame- ters were used as variables to build neural network and logistic regression model for discriminating conditions. Feature selection analysis was per- formed to reduce the number of variables. Several models were built including 3 to 10 condi- tions. Models with 5 or less conditions appeared acceptable but better performance was found with models including 3 conditions. The best accuracy was 92% for a model including ankle range of motion, fatigue and vision contrast conditions. Qualities of balance parameters were able to diag- nose impairments. However, the efficient models included only a few conditions. Models with more conditions could be built but would require a larger number of cases to reach high accuracy. The study showed that a neural network or a logistic model could be used for the diagnosis of balance impairments. Such a tool could seriously improve the prevention and rehabilitation practice.en_NZ
dc.language.isoenen_NZ
dc.publisherAuckland University of Technology
dc.subjectMedical sciencesen_NZ
dc.subjectFall (accidents) in old ageen_NZ
dc.subjectEquilibriumen_NZ
dc.subjectArtificial intelligenceen_NZ
dc.subjectSignal processingen_NZ
dc.subjectPostureen_NZ
dc.subjectDiagnosisen_NZ
dc.titleIdentification of fall-risk factor degradations using quality of balance measurementsen_NZ
dc.typeThesis
thesis.degree.grantorAuckland University of Technology
thesis.degree.grantorAuckland University of Technology
thesis.degree.levelDoctoral Theses
thesis.degree.nameDoctor of Philosophyen_NZ
thesis.degree.discipline
dc.rights.accessrightsOpenAccess
dc.date.updated2015-06-05T14:47:06Z


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record