Validation of Insole-based Gait Analysis System in Young Children with a Neurodevelopmental Disorder and Autism Traits

Ton T.H. Duong, Sylvie Goldman, Huanghe Zhang, Rachel Salazar, Sara Beenders, Kayla M. Cornett, Jennifer M. Bain, Jacqueline Montes, Damiano Zanotto

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Scopus citations

Abstract

Motor impairments are prevalent in children with neurodevelopmental disorders (NDD) and/or autism spectrum disorders (ASD) and often impact their development and quality of life. Early autism diagnosis can ameliorate lifetime outcomes by enabling earlier access to treatment. However, conventional clinical evaluation methods present some limitations due to high phenotypical heterogeneity within the autism spectrum and NDDs. Based on expanding research reporting high prevalence of motor impairments, there is a compelling need to develop quantitative methods to measure behavioral and motor-related phenotypes. Research focusing on gait analysis in individuals with ASD point to distinctive gait features. Thus, precise gait measures may contribute to more refined phenotype measures in NDD and ASD.This paper introduces the PediaSole, a minimally obtrusive insole-based system capable of measuring spatiotemporal gait parameters in pediatric populations. Accuracy of the system was validated against gold-standard equipment in neurotypical children and children with autism traits of genetic etiology, aged 3-12 yrs. Results indicated a good level of agreement between the PediaSole and the reference equipment. Additionally, 3 gait parameters (normalized stride velocity, stride time, and stance percentage) showed significant correlations with a well-established clinical measure of motor function, indicating the potential of this technology for clinical applications.

Original languageEnglish
Title of host publication2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020
Pages715-720
Number of pages6
ISBN (Electronic)9781728159072
DOIs
StatePublished - Nov 2020
Event8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020 - New York City, United States
Duration: 29 Nov 20201 Dec 2020

Publication series

NameProceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics
Volume2020-November
ISSN (Print)2155-1774

Conference

Conference8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020
Country/TerritoryUnited States
CityNew York City
Period29/11/201/12/20

Keywords

  • HNRNPH2
  • ambulatory gait analysis
  • autism spectrum disorder
  • instrumented footwear
  • machine learning regression
  • wearable technology

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