CP-AGCN: Pytorch-based attention informed graph convolutional network for identifying infants at risk of cerebral palsy

Zhang, H., Ho, E. S.L. and Shum, H. P.H. (2022) CP-AGCN: Pytorch-based attention informed graph convolutional network for identifying infants at risk of cerebral palsy. Software Impacts, 14, 100419. (doi: 10.1016/j.simpa.2022.100419)

Abstract

Early prediction is clinically considered one of the essential parts of cerebral palsy (CP) treatment. We propose to implement a low-cost and interpretable classification system for supporting CP prediction based on General Movement Assessment (GMA). We design a Pytorch-based attention-informed graph convolutional network to early identify infants at risk of CP from skeletal data extracted from RGB videos. We also design a frequency-binning module for learning the CP movements in the frequency domain while filtering noise. Our system only requires consumer-grade RGB videos for training to support interactive-time CP prediction by providing an interpretable CP classification result.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Ho, Dr Edmond S. L
• Authors: Zhang, H., Ho, E. S.L., and Shum, H. P.H.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Software Impacts
• Publisher: Elsevier
• ISSN: 2665-9638
• ISSN (Online): 2665-9638
• Published Online: 06 September 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in Software Impacts 14: 100419
• Publisher Policy: Reproduced under a Creative Commons License