Cerebral Palsy Prediction with Frequency Attention Informed Graph Convolutional Networks
This work addresses the problem of non-intrusive early CP diagnosis, particularly in resource-limited regions, though it is incremental as it builds on existing graph convolutional methods by incorporating frequency information.
The paper tackled early diagnosis of cerebral palsy by proposing a frequency attention informed graph convolutional network that leverages movement frequency differences between CP and healthy infants, achieving state-of-the-art performance on two RGB video datasets.
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.