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

There are about 2 3 CP patients in 1000 children in the UK [1], which is similar to other developed countries. Although CP cannot be completely cured at present, early prediction of CP and intervention are considered as a paramount part of the treatment. Current clinical early prediction of CP is investigated by General Movement Assessment (GMA) [2]. GMA can be done in person by GM assessors to assess an infant, or it can be done via watching an RGB video that has recorded the general movements of the infant. However, the GMA training is time-and resourceconsuming, making it challenging to cope with the high demand for CP prediction. To tackle this problem, we propose automating this process by analyzing the general movements of infants from RGB videos. This allows the early prediction to cover even the lower-risk population. Motivated by the encouraging results reported in recent research based on skeletal data [3, 4, 5, 6, 7, 8, 9, 10], the 2D joint locations of the infant are extracted from RGB videos as the input of the system for CP prediction. The computational intelligence of our system is implemented with a graph convolution network, a kind of deep artificial neural network that models relational data very well, making it suitable for skeleton data.