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

arXiv: https://arxiv.org/abs/2209.02824 · HTML: https://mlsanity.com/paper/2209.02824/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 6 Sep 2022.

## Builds on (most cited first, 11 of 11)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [RMPE: Regional Multi-person Pose Estimation](https://mlsanity.com/paper/1612.00137/) (2016)
- [A Spatio-temporal Attention-based Model for Infant Movement Assessment from Videos](https://mlsanity.com/paper/2105.09783/) (2021)
- [A Camera That CNNs: Towards Embedded Neural Networks on Pixel Processor Arrays](https://mlsanity.com/paper/1909.05647/) (2019)
- [Interpreting Deep Learning based Cerebral Palsy Prediction with Channel Attention](https://mlsanity.com/paper/2106.04471/) (2021)
- [Towards Explainable Abnormal Infant Movements Identification: A Body-part Based Prediction and Visualisation Framework](https://mlsanity.com/paper/2106.04966/) (2021)
- [Cerebral Palsy Prediction with Frequency Attention Informed Graph Convolutional Networks](https://mlsanity.com/paper/2204.10997/) (2022)
- [Openmv: A Python powered, extensible machine vision camera](https://mlsanity.com/paper/1711.10464/) (2017)
- [Pose-based Tremor Classification for Parkinson's Disease Diagnosis from Video](https://mlsanity.com/paper/2207.06828/) (2022)

## Built on by (most cited first, 3 of 3)

- [PD-ADSV: An Automated Diagnosing System Using Voice Signals and Hard Voting Ensemble Method for Parkinson's Disease](https://mlsanity.com/paper/2304.06016/) (2023)
- [Evaluating Explainable AI Methods in Deep Learning Models for Early Detection of Cerebral Palsy](https://mlsanity.com/paper/2409.00001/) (2024)
- [Towards Biomarker Discovery for Early Cerebral Palsy Detection: Evaluating Explanations Through Kinematic Perturbations](https://mlsanity.com/paper/2503.16452/) (2025)

## Abstract (arXiv)

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.