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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
Physiotherapy Department, Epworth Healthcare, Melbourne, Australia
Traumatic Brain Injury
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.
DOI: 10.1097/HTR.0000000000000038
PubMed URL:
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

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