Wearable Sensor-Based Health Monitoring Using Artificial Intelligence: A Smart Healthcare Framework for Continuous Patient Monitoring

The increasing prevalence of chronic diseases, aging populations, and the growing demand for remote healthcare services have accelerated the adoption of wearable sensor technologies in modern healthcare systems. Wearable devices equipped with physiological sensors enable continuous monitoring of vital parameters such as heart rate, blood oxygen saturation, body temperature, physical activity, and sleep patterns. When integrated with Artificial Intelligence (AI), these systems can transform raw sensor data into meaningful clinical insights, supporting early disease detection, personalized healthcare, and timely medical intervention. This paper proposes a smart healthcare framework that combines wearable sensing technologies, Internet of Things (IoT) connectivity, cloud-based data management, and AI-driven analytics for remote patient monitoring. The proposed architecture includes data acquisition, preprocessing, feature extraction, machine learning-based health assessment, and alert generation modules. A comparative analysis of existing healthcare monitoring approaches highlights current limitations, including fragmented data management, limited predictive capabilities, and privacy concerns. The proposed framework is designed to address these challenges through intelligent data processing and scalable remote monitoring capabilities. The study also discusses implementation challenges, security considerations, and future research directions involving Explainable Artificial Intelligence (XAI), Federated Learning, and Edge Computing. Future work will focus on experimental validation using publicly available healthcare datasets such as PhysioNet, MIMIC-IV, WESAD, and PAMAP2, along with real-world wearable sensing platforms.

Wearable Sensors, Artificial Intelligence, Remote Patient Monitoring, Smart Healthcare, Internet of Things, Machine Learning, Health Analytics

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