Validation of low-cost system for gait assessment in children with ataxia. (November 2020)

• Record Type: Journal Article
• Authors: Summa, S.; Tartarisco, G.; Favetta, M.; Buzachis, A.; Romano, A.; Bernava, G.M.; Sancesario, A.; Vasco, G.; Pioggia, G.; Petrarca, M.; Castelli, E.; Bertini, E.; Schirinzi, T.
• Is Part Of: Computer methods and programs in biomedicine. Volume 196(2020)
• Publication Date: 2020-11
• Subjects: Gait analysis -- Bland–Altman test -- Spatio-temporal parameters -- Ataxia -- Kinect; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105705
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The spatio-temporal parameters measured by Kinect can provide fundamental information in clinical practice. The Speed and the Stride Length correlate with the clinical scale SARA. A machine learning approach combined with the Kinect shows a great potential to automatically assess the gait gravity. Kinect is a tool useful to assess disease severity biomarkers in the framework of the "at-home monitoring systems". Abstract: 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.