Innovative Research on Identifying the Potential Association between Emotional Abnormalities and Physiological Illnesses in Young Children: Construction of an Artificial Intelligence-Based Recognition Model

Background/Objectives: Health care in preschools has gained increasing attention, particularly in the post-pandemic era, as educators face dual challenges in detecting emotional and physiological abnormalities among young children. Observation-based assessments are subjective and lack real-time data, often delaying the identification of potential health risks. This study aimed to construct an artificial intelligence (AI)-based model capable of recognizing the potential association between emotional abnormalities and physiological illnesses in preschool children. Methods: A mixed-method design was employed, integrating a literature review and the Delphi method. The literature review identified trends and feasibility in AI-assisted child health monitoring. Nine interdisciplinary experts in pediatrics, AI sensing, and early childhood education participated in three Delphi rounds to establish consensus on key physiological and behavioral indicators. Results: Experts reached consensus on five primary indicators—facial expression, speech prosody, heart rate variability (HRV), galvanic skin response (GSR), and skin temperature—and recommended using noninvasive wearable devices. A real-time risk alert system using red, yellow, and green levels was proposed. The final AI model included four modules: sensor input, data pre-processing, AI integration and analysis, and feedback interface. Conclusions: The developed AI-based recognition model demonstrates strong potential for early detection of emotional and physiological abnormalities in preschoolers. It provides timely, objective, and science-based support for caregivers, facilitating early intervention and individualized care. This model may serve as a practical framework for advancing digital transformation in preschool health care.