Automated assessment of gait deviations in children with cerebral palsy using a sensorized shoe and Active Shape Models

Abstract

Periodic assessments of motor function in children with Cerebral Palsy can enable clinicians to make more informed decisions about the type and timing of treatment interventions. Current clinical practice is limited to sporadic assessments performed in a clinical environment and hence, not suitable for capturing small changes that occur longitudinally. We have developed a shoe-based wearable sensor system that allows unobtrusive long-term collection of center of pressure data in the home setting. So far the shoe-based system has been used to collect data from 15 subjects under supervised and semi-supervised settings. In this paper, we present a novel methodology, based on the analysis of center of pressure trajectories using Active Shape Models, for automated clinical assessment of gait deviations in children with Cerebral Palsy. We show that Active Shape Models can be used to effectively model characteristics of the center of pressure trajectories that are associated with specific aspects of gait deviations. A support vector machine classifier, trained on features derived from the Active Shape Models, is able to achieve an accuracy of greater than 90% at classifying clinical scores of gait deviation severity.