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.

翻译：可靠的神经发育评估方法对于及早发现需及时干预的医学问题至关重要。自发性运动活动（即"运动学"）已被证明可为未来神经发育提供强有力的替代评估指标。然而，其评估方法主要依赖定性及主观判断，侧重于通过视觉识别的特定月龄手势。本研究采用替代性方法，通过数据驱动的个体运动模式评估来预测婴儿神经发育成熟度。我们利用经姿态估计处理的婴儿三维视频记录，提取解剖标志点的时空序列，并应用自适应图卷积网络预测实际月龄。研究证实，我们的数据驱动方法相较于基于人工设计特征的传统机器学习基线模型具有显著优势。