Classification of Movement Disorders Using Video Recordings of Gait with Attention-based Graph Convolutional Networks

Wei Tang*, Peter M.A. Van Ooijen, Deborah A. Sival, Natasha M. Maurits

*Corresponding author for this work

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

2 Downloads (Pure)

Abstract

Early Onset Ataxia (EOA) and Developmental Coordination Disorder (DCD) are two pediatric movement disorders characterized by similar phenotypic traits, often complicating clinical differential diagnostics. Despite the recognized reliability of current clinical scales like the Scale for the Assessment and Rating of Ataxia (SARA), their dependence on specialist expertise, time-consuming nature, and inherent subjectivity can potentially limit their efficacy in assessing movement disorders, thereby underscoring the need for more objective, and efficient diagnostic methods. This study introduces a novel approach that utilizes 2D video recording in the coronal plane coupled with pose estimation to differentiate gait patterns in children with EOA, DCD, and healthy controls (HC). An attention-based Graph Convolutional Network (A-GCN) was proposed for the classification process, achieving an f1-score of 76% at the group level. The model incorporates channel-wise attention to stress the semantic nuances of body joints, and temporal attention to highlight important sequences in gait patterns. These mechanisms enhance the model's ability to accurately classify EOA and DCD. Our results demonstrate the potential of this method to improve diagnosis and understanding of movement disorders, thereby paving the way for more targeted treatment strategies. The code is available at https://github.com/jiudaa/Attention-basedGCN-EOA.git.

Original languageEnglish
Title of host publicationBHI 2023 - IEEE-EMBS International Conference on Biomedical and Health Informatics, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9798350310504
DOIs
Publication statusPublished - Nov-2023
Event2023 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2023 - Pittsburgh, United States
Duration: 15-Oct-202318-Oct-2023

Publication series

NameBHI 2023 - IEEE-EMBS International Conference on Biomedical and Health Informatics, Proceedings

Conference

Conference2023 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2023
Country/TerritoryUnited States
CityPittsburgh
Period15/10/202318/10/2023

Keywords

  • deep learning
  • Developmental Coordination Disorder (DCD)
  • Early Onset Ataxia (EOA)
  • Graph Convolutional Network (GCN)

Cite this