CP-AGCN: Pytorch-based Attention Informed Graph Convolutional Network for Identifying Infants at Risk of Cerebral Palsy
This work addresses the problem of early cerebral palsy diagnosis for infants and clinicians, offering an incremental improvement through a novel method for processing video data.
The researchers tackled early prediction of cerebral palsy in infants by developing a low-cost, interpretable classification system using skeletal data from RGB videos, achieving interactive-time prediction with their attention-informed graph convolutional network and frequency-binning module.
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.