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DC Field | Value | Language |
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dc.contributor.author | Williams, Gavin | en |
dc.contributor.author | Morris, Meg | en |
dc.contributor.other | Lai, Daniel | en |
dc.contributor.other | Schache, Anthony | en |
dc.date.accessioned | 2014-08-24T23:52:26Z | en |
dc.date.available | 2014-08-24T23:52:26Z | en |
dc.date.issued | 2015-03 | en |
dc.identifier.citation | The Journal of Head Trauma Rehabilitation. Vol.30(2), Mar-Apr 2015, pp. E13-E23. | en |
dc.identifier.issn | 0885-9701 | en |
dc.identifier.uri | http://hdl.handle.net/11434/56 | en |
dc.description.abstract | OBJECTIVE:: To determine the extent to which gait disorders associated with traumatic brain injury (TBI) are able to be classified into clinically relevant and distinct subgroups. DESIGN:: Cross-sectional cohort study comprising people with TBI receiving physiotherapy for mobility limitations. PARTICIPANTS:: One hundred two people with TBI. OUTCOME MEASURES:: The taxonomic framework for gait disorders following TBI was devised on the basis of a framework previously developed for people with cerebral palsy. Participants with TBI who were receiving therapy for mobility problems were assessed using 3-dimensional gait analysis. Pelvis and bilateral lower limb kinematic data were recorded using a VICON motion analysis system while each participant walked at a self-selected speed. Five trials of data were collected for each participant. Multiclass support vector machine models were developed to systematically and automatically ascertain the clinical classification. RESULTS:: The statistical features derived from the major joint angles from unaffected limbs contributed to the best classification accuracy of 82.35% (84 of the 102 subjects). Features from the affected limb resulted in a classification accuracy of 76.47% (78 of 102 subjects). CONCLUSIONS:: Despite considerable variability in gait disorders following TBI, we were able to generate a clinical classification system on the basis of 6 distinct subgroups of gait deviations. Statistical features related to the motion of the pelvis, hip, knee, and ankle on the less affected leg were able to accurately classify 82% of people with TBI-related gait disorders using a multiclass support vector machine framework. | en |
dc.subject | Assessment | en |
dc.subject | Gait Disorders | en |
dc.subject | Brain Trauma | en |
dc.subject | Classification | en |
dc.subject | Rehabilitation | en |
dc.subject | Physiotherapy Department, Epworth Healthcare, Melbourne, Australia | - |
dc.subject | TBI | - |
dc.subject | Traumatic Brain Injury | - |
dc.subject | Mobility | - |
dc.subject | 3-Dimensional Gait Analysis | - |
dc.subject | Kinematic Data | - |
dc.subject | VICON motion analysis | - |
dc.title | Classification of gait disorders following traumatic brain injury. | en |
dc.type | Journal Article | en |
dc.identifier.doi | 10.1097/HTR.0000000000000038 | en |
dc.identifier.journaltitle | Journal of Head Trauma and Rehabilitation | en |
dc.description.pubmeduri | http://www.ncbi.nlm.nih.gov/pubmed/24695264 | en |
dc.type.studyortrial | Cross-sectional Cohort Study | - |
Appears in Collections: | Neurosciences Rehabilitation |
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