CP-AGCN: Pytorch-based Attention Informed Graph Convolutional Network for Identifying Infants at Risk of Cerebral Palsy

TL;DR

This paper introduces CP-AGCN, a Pytorch-based attention-informed graph convolutional network that predicts cerebral palsy risk in infants using skeletal data from RGB videos, emphasizing interpretability and low-cost implementation.

Contribution

It presents a novel graph convolutional network with attention mechanisms and frequency-binning for early, interpretable CP prediction from simple RGB videos.

Findings

Achieved accurate early CP risk classification from RGB videos.

Demonstrated the effectiveness of frequency-binning in filtering noise.

Provided a low-cost, interpretable system suitable for clinical use.

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.