# Cerebral Palsy Prediction with Frequency Attention Informed Graph Convolutional Networks

arXiv: https://arxiv.org/abs/2204.10997 · HTML: https://mlsanity.com/paper/2204.10997/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 26 references that are in the map.
- First posted on arXiv 23 Apr 2022.

## Builds on (most cited first, 6 of 6)

- [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)
- [A Spatio-temporal Attention-based Model for Infant Movement Assessment from Videos](https://mlsanity.com/paper/2105.09783/) (2021)
- [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)

## Built on by (most cited first, 6 of 6)

- [CP-AGCN: Pytorch-based Attention Informed Graph Convolutional Network for Identifying Infants at Risk of Cerebral Palsy](https://mlsanity.com/paper/2209.02824/) (2022)
- [Lightweight Neural Architecture Search for Cerebral Palsy Detection](https://mlsanity.com/paper/2409.20060/) (2024)
- [UDF-GMA: Uncertainty Disentanglement and Fusion for General Movement Assessment](https://mlsanity.com/paper/2507.04814/) (2025)
- [INCLG: Inpainting for Non-Cleft Lip Generation with a Multi-Task Image Processing Network](https://mlsanity.com/paper/2305.10589/) (2023)
- [Towards Biomarker Discovery for Early Cerebral Palsy Detection: Evaluating Explanations Through Kinematic Perturbations](https://mlsanity.com/paper/2503.16452/) (2025)
- [Learning Developmental Age from 3D Infant Kinetics Using Adaptive Graph Neural Networks](https://mlsanity.com/paper/2402.14400/) (2024)

## Abstract (arXiv)

Early diagnosis and intervention are clinically considered the paramount part of treating cerebral palsy (CP), so it is essential to design an efficient and interpretable automatic prediction system for CP. We highlight a significant difference between CP infants' frequency of human movement and that of the healthy group, which improves prediction performance. However, the existing deep learning-based methods did not use the frequency information of infants' movement for CP prediction. This paper proposes a frequency attention informed graph convolutional network and validates it on two consumer-grade RGB video datasets, namely MINI-RGBD and RVI-38 datasets. Our proposed frequency attention module aids in improving both classification performance and system interpretability. In addition, we design a frequency-binning method that retains the critical frequency of the human joint position data while filtering the noise. Our prediction performance achieves state-of-the-art research on both datasets. Our work demonstrates the effectiveness of frequency information in supporting the prediction of CP non-intrusively and provides a way for supporting the early diagnosis of CP in the resource-limited regions where the clinical resources are not abundant.