Related Experiment Video
Updated: May 1, 2026

Automated Gait Analysis to Assess Functional Recovery in Rodents with Peripheral Nerve or Spinal Cord Contusion Injury
Published on: October 6, 2020
Classification of gait disorders following traumatic brain injury
Gavin Williams1, Daniel Lai, Anthony Schache
1Epworth Healthcare and The University of Melbourne, Melbourne, Australia (Dr Williams); Victoria University, Melbourne, Australia (Dr Lai); School of Mechanical Engineering, The University of Melbourne, Australia (Dr Schache); and School of Allied Health, La Trobe University, Melbourne, Australia (Dr Morris).
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.
Related Concept Videos
Traumatic Brain Injury l: Introduction
Secondary Spinal Cord Injury llI: Pathophysiology
Spinal Cord Injury ll: Pathophysiology

