Gait analysis in spastic hemiplegia and diplegia cerebral palsy using a wearable activity tracking device- a data quality analysis for deep convolutional neural networks

Poonam Kumari, Nicholas J. Cooney, Tae-Seong Kim, Atul S. Minhas

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

Cerebral Palsy (CP) is a movement disorder in children and affects their course of physical activity as they move to adulthood. Clinical gait analysis is the most acceptable technique to quantify and understand the defects in their gait. We developed a prototype of a wearable physical activitytracking device for gait analysis in cerebral palsy. Our device is a manifestation of a recently proposed architecture on Wearable Internet of Things (WIoT). We demonstrate the design of our device and illustrate two sets of imitated data collected by a volunteer for spastic hemiplegia and diplegia, which are two different types of CP. We sampled the data every 120ms to preserve the pattern of motion. We prepared our data in one and two dimensions to present the data to a deep convolutional neural network. The data shows some variations between different groups and opens a channel for future research.

Original languageEnglish
Title of host publicationProceedings
Subtitle of host publication2018 5th Asia-Pacific World Congress on Computer Science and Engineering: APWConCSE 2018
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages295-298
Number of pages4
ISBN (Electronic)9781728113906
ISBN (Print)9781728113913
DOIs
Publication statusPublished - 2019
Event5th Asia-Pacific World Congress on Computer Science and Engineering, APWC on CSE 2018 - Nadi, Fiji
Duration: 10 Dec 201812 Dec 2018

Conference

Conference5th Asia-Pacific World Congress on Computer Science and Engineering, APWC on CSE 2018
CountryFiji
CityNadi
Period10/12/1812/12/18

Keywords

  • Cerebral Palsy
  • WIoT
  • IoT
  • GMFCS

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