Background: Ataxic syndromes include several rare, inherited and acquired conditions. One of the main issues is the absence of specific, and sensitive automatic evaluation tools and digital outcome measures to obtain a continuous monitoring of subjects' motor ability.
Objectives: This study aims to test the usability of the Kinect system for assessing ataxia severity, exploring the potentiality of clustering algorithms and validating this system with a standard motion capture system.
Methods: Gait evaluation was performed by standardized gait analysis and by Kinect v2 during the same day in a cohort of young patient (mean age of 13.8±7.2). We analyzed the gait spatio-temporal parameters and we looked at the differences between the two systems through correlation and agreement tests. As well, we tested for possible correlations with the SARA scale as well. Finally, standard classification algorithm and principal components analysis were used to discern disease severity and groups.
Results: We found biases and linear relationships between all the parameters. Significant correlations emerged between the SARA and the Speed, the Stride Length and the Step Length. PCA results, highlighting that a machine learning approach combined with Kinect-based evaluation shows great potential to automatically assess disease severity and diagnosis.
Conclusions: The spatio-temporal parameters measured by Kinect cannot be used interchangeably with those parameters acquired with standard motion capture system in clinical practice but can still provide fundamental information. Specifically, these results might bring to the development of a novel system to perform easy and quick evaluation of gait in young patients with ataxia, useful for patients stratification in terms of clinical severity and diagnosis.
Keywords: Ataxia; Bland–Altman test; Gait analysis; Kinect; Spatio-temporal parameters.
Copyright © 2020. Published by Elsevier B.V.