  Reliable methods for the neurodevelopmental assessment of infants are
essential for early detection of medical issues that may need prompt
interventions. Spontaneous motor activity, or 'kinetics', is shown to provide a
powerful surrogate measure of upcoming neurodevelopment. However, its
assessment is by and large qualitative and subjective, focusing on visually
identified, age-specific gestures. Here, we follow an alternative approach,
predicting infants' neurodevelopmental maturation based on data-driven
evaluation of individual motor patterns. We utilize 3D video recordings of
infants processed with pose-estimation to extract spatio-temporal series of
anatomical landmarks, and apply adaptive graph convolutional networks to
predict the actual age. We show that our data-driven approach achieves
improvement over traditional machine learning baselines based on manually
engineered features.
