Real-time semantic interpretation of IoT sensor data for patient health monitoring

The Internet of Things enables continuous acquisition of physiological data through wearable and environmental sensors, creating new opportunities for early risk detection and timely clinical intervention. However, most IoT-based healthcare monitoring systems lack real-time semantic interpretation, limiting the transformation of raw sensor measurements into clinically meaningful and actionable information. This paper proposes an ontology-driven real-time architecture for semantic interpretation of IoT sensor data in healthcare monitoring scenarios. The system integrates streaming data ingestion, in-stream semantic classification, composite health condition inference, and scalable persistence within a unified pipeline. Physiological parameters such as heart rate, blood oxygen saturation, respiratory rate, heart rate variability, blood pressure, sleep duration, and physical activity are classified using guideline-based thresholds, while higher-level composite conditions are inferred from co-occurring semantic statuses. The approach is experimentally validated using a hybrid dataset composed of real-world wearable data and simulated high-throughput sensor streams. Results show that the complete pipeline processes 1000 observations with an average end-to-end latency of approximately 195 ms, enabling near real-time and deterministic semantic interpretation and visualization. A semantic correctness analysis based on predefined clinical scenarios confirms consistent and reproducible mapping between physiological measurements and ontology-defined health statuses. These findings demonstrate that ontology-based semantic interpretation can be operationalized within real-time IoT healthcare pipelines, significantly improving interpretability, responsiveness, and clinical relevance. The proposed architecture provides a practical and scalable foundation for semantically enriched patient health monitoring and supports future integration with standardized clinical data ecosystems.