  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.
