# Learning Developmental Age from 3D Infant Kinetics Using Adaptive Graph Neural Networks

arXiv: https://arxiv.org/abs/2402.14400 · HTML: https://mlsanity.com/paper/2402.14400/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 22 Feb 2024.

## Builds on (most cited first, 10 of 10)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Residual Attention Network for Image Classification](https://mlsanity.com/paper/1704.06904/) (2017)
- [Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional Networks](https://mlsanity.com/paper/1912.06971/) (2019)
- [MoMo: Momentum Models for Adaptive Learning Rates](https://mlsanity.com/paper/2305.07583/) (2023)
- [Cerebral Palsy Prediction with Frequency Attention Informed Graph Convolutional Networks](https://mlsanity.com/paper/2204.10997/) (2022)
- [Semi-supervised Body Parsing and Pose Estimation for Enhancing Infant General Movement Assessment](https://mlsanity.com/paper/2210.08054/) (2022)
- [Automated Classification of General Movements in Infants Using a Two-stream Spatiotemporal Fusion Network](https://mlsanity.com/paper/2207.03344/) (2022)

## Abstract (arXiv)

Reliable methods for the neurodevelopmental assessment of infants are essential for early detection of problems 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. In this work, we introduce Kinetic Age (KA), a novel data-driven metric that quantifies neurodevelopmental maturity by predicting an infant's age based on their movement patterns. KA offers an interpretable and generalizable proxy for motor development. Our method leverages 3D video recordings of infants, processed with pose estimation to extract spatio-temporal series of anatomical landmarks, which are released as a new openly available dataset. These data are modeled using adaptive graph convolutional networks, able to capture the spatio-temporal dependencies in infant movements. We also show that our data-driven approach achieves improvement over traditional machine learning baselines based on manually engineered features.