  Early prediction of cerebral palsy is essential as it leads to early
treatment and monitoring. Deep learning has shown promising results in
biomedical engineering thanks to its capacity of modelling complicated data
with its non-linear architecture. However, due to their complex structure, deep
learning models are generally not interpretable by humans, making it difficult
for clinicians to rely on the findings. In this paper, we propose a channel
attention module for deep learning models to predict cerebral palsy from
infants' body movements, which highlights the key features (i.e. body joints)
the model identifies as important, thereby indicating why certain diagnostic
results are found. To highlight the capacity of the deep network in modelling
input features, we utilize raw joint positions instead of hand-crafted
features. We validate our system with a real-world infant movement dataset. Our
proposed channel attention module enables the visualization of the vital joints
to this disease that the network considers. Our system achieves 91.67%
accuracy, suppressing other state-of-the-art deep learning methods.
