Smart Wearable Embedded Systems For Al-Based Monitoring Of Children's Vital Signs And Health Conditions

Abstract

The increasing need for continuous and non-invasive monitoring of children's health has created significant interest in smart wearable technologies capable of collecting and analysing physiological information in real time. Conventional clinical monitoring methods are generally dependent on hospital-based equipment and periodic assessments, which may limit continuous observation of changes in a child's vital signs during daily activities. This study proposes a smart wearable embedded system integrated with artificial intelligence (AI) for continuous monitoring and intelligent assessment of children's vital signs and health conditions. The proposed system combines wearable physiological sensors, an embedded processing unit, wireless communication, and an AI-based analytical framework to acquire and interpret parameters such as heart rate, body temperature, blood oxygen saturation, respiratory rate, and activity-related information. The acquired physiological data are processed to reduce noise and identify abnormal patterns, while the AI model evaluates variations in multiple parameters to support early identification of potentially concerning health conditions. Unlike conventional threshold-based monitoring, the proposed approach considers the temporal and combined behaviour of physiological parameters, thereby enabling more adaptive health assessment. A connected monitoring architecture is further incorporated to facilitate real-time transmission of processed information to caregivers or authorised healthcare personnel. The proposed framework is intended to provide timely alerts while maintaining low power consumption, portability, and suitability for continuous use by children. The study demonstrates how the integration of AI with wearable embedded systems can contribute to proactive paediatric health monitoring, early anomaly detection, and improved accessibility to physiological information outside conventional clinical environments. The proposed approach provides a foundation for developing intelligent, reliable, and scalable wearable healthcare systems for continuous child health surveillance.