Modeling 3D Infant Kinetics Using Adaptive Graph Convolutional Networks
<|reference_start|>Modeling 3D Infant Kinetics Using Adaptive Graph Convolutional Networks: 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.<|reference_end|>
@article{holmberg2024modeling,
    title={Modeling 3D Infant Kinetics Using Adaptive Graph Convolutional Networks},
    author={Daniel Holmberg, Manu Airaksinen, Viviana Marchi, Andrea Guzzetta,
  Anna Kivi, Leena Haataja, Sampsa Vanhatalo, Teemu Roos},
    journal={arXiv preprint arXiv:2402.14400},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.14400},
    primaryClass={cs.CV cs.LG eess.IV}
}