Child Health Monitoring Through Wearable Technologies And Real-Time Physiological Data Analytics

Abstract

Background: Wearable technologies and real-time physiological data analytics are promising avenues for continuous health monitoring in pediatric patients. These systems enable the collection of critical health metrics, leading to the early detection of physiological abnormalities, thus encouraging proactive healthcare and timely clinical responses. Objective: The purpose of this study is to assess the efficiency of wearable devices and real-time analytics in monitoring child health, detecting critical physiological indicators, and predicting potential health risks with the help of machine learning techniques. Methodology: A longitudinal observational study was carried out between 2023 and 2025, involving 3000 children aged 5–15 years. Wearable sensors continuously monitored heart rate, blood oxygen saturation (SpO 2), body temperature, respiratory rate, physical activity and sleep. Statistical analysis and machine learning models such as Random Forest, XGBoost and Long Short-Term Memory (LSTM) were applied for health-risk prediction and anomaly detection.  Findings: Analysis of over 150 million physiological records. The mean heart rate was 88 bpm, SpO₂ was 97.2%, and sleep was 8.1 hours. LSTM had the best prediction accuracy at 96.2%, followed by XGBoost (94.5%) and Random Forest (92.8%). Heart rate variability was the strongest predictor of health risk.  Conclusion: Wearable technologies coupled with real-time analytics offer a powerful platform for continuous child health monitoring, facilitating early risk detection, personalized health interventions, and better pediatric health outcomes