Modeling 3D Infant Kinetics Using Adaptive Graph Convolutional Networks

• URL: http://arxiv.org/abs/2402.14400v2
• Date: Thu, 20 Jun 2024 06:34:06 GMT
• Title: Modeling 3D Infant Kinetics Using Adaptive Graph Convolutional Networks
• Authors: Daniel Holmberg, Manu Airaksinen, Viviana Marchi, Andrea Guzzetta, Anna Kivi, Leena Haataja, Sampsa Vanhatalo, Teemu Roos,
• Abstract summary: Spontaneous motor activity, orkinetics', is shown to provide a powerful surrogate measure of upcoming neurodevelopment. Here, we follow an alternative approach, predicting infants' maturation based on data-driven evaluation of individual motor patterns.
• Score: 2.2279946664123664
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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