Computational tools for analyzing infant biometric and environmental data

Recent advances in wearable and portable technologies have substantially increased the quantity and diversity of data available for monitoring and evaluating infant behavior. This review presents a range of computational tools and techniques for handling dense recordings of behavioral, physiological, and environmental variables in everyday settings. We focus on the analysis of emerging wearable and environmental devices that enable naturalistic, at-home assessment of infant behavior and physiology. Devices that collect data across infants’ daily lives help address the limitations of traditional data collection methods, which typically rely on time-limited sessions in unfamiliar clinical or laboratory settings that may bias recorded behavior. These technologies have expanded the types and volumes of information collected, creating a growing need for comprehensive computational analysis pipelines. Analytical frameworks must be designed to accommodate the heterogeneity of these datasets, which arise from their subject-specific and context-dependent nature. Additionally, the data may be incomplete, noisy, or unreliable. We discuss approaches, including hierarchical models for interpretable inference at both population and individual levels, deep learning methods for predictive modeling, and state-based models for tracking behavioral and physiological states over time. This review highlights emerging opportunities to study infants in naturalistic settings by aligning wearable and environmental sensing with analytic goals of inference, prediction, and state-tracking.