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.

翻译：可靠的婴儿神经发育评估方法对于早期发现可能需要及时干预的问题至关重要。自发性运动活动（或称"运动学"）已被证明能够作为评估即将到来的神经发育的有力替代指标。然而，目前的评估方法大多是定性且主观的，主要依赖于视觉识别的、特定年龄段的姿态动作。在本研究中，我们提出了"运动年龄"这一新颖的数据驱动指标，该指标通过基于婴儿运动模式预测其年龄来量化神经发育成熟度。KA为运动发育提供了一个可解释且可泛化的代理指标。我们的方法利用婴儿的三维视频记录，通过姿态估计算法提取解剖标志点的时空序列，这些数据已作为新的开放数据集发布。我们采用自适应图卷积网络对这些数据进行建模，该网络能够捕捉婴儿运动中的时空依赖关系。我们还证明，相较于基于人工设计特征的传统机器学习基线方法，我们的数据驱动方法取得了性能提升。