@article{ZHANG2022100419,
title = {CP-AGCN: Pytorch-based attention informed graph convolutional network for identifying infants at risk of cerebral palsy},
journal = {Software Impacts},
volume = {14},
pages = {100419},
year = {2022},
issn = {2665-9638},
doi = {https://doi.org/10.1016/j.simpa.2022.100419},
url = {https://www.sciencedirect.com/science/article/pii/S2665963822001038},
author = {Haozheng Zhang and Edmond S.L. Ho and Hubert P.H. Shum},
keywords = {Disease prediction, Graph convolutional network, Classification, Human motion analysis},
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.}
}