Integrating Wearable Device Data with AI for Real-Time Health Monitoring

Advances in wearable sensing technologies have enabled continuous collection of physiological and behavioral data, creating new opportunities for real-time health monitoring outside clinical environments. This study presents an integrated framework that combines wearable device data with artificial intelligence to support early detection of health deviations and timely intervention. The system processes multi-modal signals, including heart rate, physical activity, sleep patterns, and peripheral temperature, using adaptive filtering and lightweight machine-learning models designed for streaming data. Evaluation on publicly available and pilot-collected datasets demonstrates that the framework can capture subtle changes in vital-sign dynamics and reliably flag emerging risks with low latency. The results indicate strong potential for enhancing personalized health management, supporting remote patient monitoring, and reducing the burden on healthcare facilities. The study also discusses implementation challenges, including data quality variation, interoperability, and privacy protection, which are critical for large-scale deployment. Overall, the findings underscore the role of AI-enabled wearable monitoring systems in advancing proactive and patient-centered healthcare.