Conference Agenda

Traditional health monitoring systems rely on periodic sampling, which introduces significant delays in detecting critical physiological events. This paper presents an IoT-based biomedical architecture integrating real-time streaming analytics to enable early detection of adverse health conditions. The proposed system combines wearable biomedical sensors, MQTT communication protocols, and Apache Kafka-based streaming pipelines for continuous physiological data processing. We evaluated the architecture through controlled simulations comparing streaming analytics against conventional periodic sampling approaches as a theorical approach. Results demonstrate a substantial reduction in event detection latency, with the streaming system achieving mean detection delays of 47.0 seconds compared to 300.0 seconds for periodic sampling (p < 0.001). The MQTT-based communication layer exhibited mean latency of 30.1 ms with 95th percentile at 56.8 ms, significantly outperforming HTTP alternatives (mean: 149.0 ms, 95th percentile: 289.5 ms). System scalability testing revealed linear throughput scaling, supporting up to 100 concurrent sensors at 88,000 messages per second. These findings validate the efficacy of streaming analytics in biomedical IoT systems for preventive healthcare applications, with relevance for continuous monitoring of cardiac, respiratory, and metabolic parameters.