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http://hdl.handle.net/11434/56
Title: | Classification of gait disorders following traumatic brain injury. |
Epworth Authors: | Williams, Gavin Morris, Meg |
Other Authors: | Lai, Daniel Schache, Anthony |
Keywords: | Assessment Gait Disorders Brain Trauma Classification Rehabilitation Physiotherapy Department, Epworth Healthcare, Melbourne, Australia TBI Traumatic Brain Injury Mobility 3-Dimensional Gait Analysis Kinematic Data VICON motion analysis |
Issue Date: | Mar-2015 |
Citation: | The Journal of Head Trauma Rehabilitation. Vol.30(2), Mar-Apr 2015, pp. E13-E23. |
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. |
URI: | http://hdl.handle.net/11434/56 |
DOI: | 10.1097/HTR.0000000000000038 |
PubMed URL: | http://www.ncbi.nlm.nih.gov/pubmed/24695264 |
ISSN: | 0885-9701 |
Journal Title: | Journal of Head Trauma and Rehabilitation |
Type: | Journal Article |
Type of Clinical Study or Trial: | Cross-sectional Cohort Study |
Appears in Collections: | Neurosciences Rehabilitation |
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