General movements (GMs) are spontaneous, coordinated body movements in infants that offer valuable insights into the developing nervous system. Assessed through the Prechtl GM Assessment (GMA), GMs are reliable predictors for neurodevelopmental disorders. However, GMA requires specifically trained clinicians, who are limited in number. To scale up newborn screening, there is a need for an algorithm that can automatically classify GMs from infant video recordings. This data poses challenges, including variability in recording length, device type, and setting, with each video coarsely annotated for overall movement quality. In this work, we introduce a tool for extracting features from these recordings and explore various machine learning techniques for automated GM classification.

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.

Aiming at objective early detection of neuromotor disorders such as cerebral palsy, we proposed an innovative non-intrusive approach using a pressure sensing device to classify infant general movements (GMs). Here, we tested the feasibility of using pressure data to differentiate typical GM patterns of the ''fidgety period'' (i.e., fidgety movements) vs. the ''pre-fidgety period'' (i.e., writhing movements). Participants (N = 45) were sampled from a typically-developing infant cohort. Multi-modal sensor data, including pressure data from a 32x32-grid pressure sensing mat with 1024 sensors, were prospectively recorded for each infant in seven succeeding laboratory sessions in biweekly intervals from 4-16 weeks of post-term age. For proof-of-concept, 1776 pressure data snippets, each 5s long, from the two targeted age periods were taken for movement classification. Each snippet was pre-annotated based on corresponding synchronised video data by human assessors as either fidgety present (FM+) or absent (FM-). Multiple neural network architectures were tested to distinguish the FM+ vs. FM- classes, including support vector machines (SVM), feed-forward networks (FFNs), convolutional neural networks (CNNs), and long short-term memory (LSTM) networks. The CNN achieved the highest average classification accuracy (81.4%) for classes FM+ vs. FM-. Comparing the pros and cons of other methods aiming at automated GMA to the pressure sensing approach, we concluded that the pressure sensing approach has great potential for efficient large-scale motion data acquisition and sharing. This will in return enable improvement of the approach that may prove scalable for daily clinical application for evaluating infant neuromotor functions.

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