Modeling 3D Infant Kinetics Using Adaptive Graph Convolutional Networks
Daniel Holmberg Manu Airaksinen Viviana Marchi Andrea Guzzetta Anna Kivi Leena Haataja Sampsa Vanhatalo Teemu Roos
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.