Search papers, labs, and topics across Lattice.
This paper introduces an event-driven, cloud-native framework that efficiently ingests and normalizes high-frequency vital signs from diverse consumer-grade wearables, addressing challenges of device heterogeneity and regulatory compliance. By employing a multi-layered microservice architecture and a dependency-aware FHIR minimization scheme, the system achieves lossless data reconstruction while significantly reducing storage overhead. Performance evaluations reveal the platform can handle 50 ingestion requests per second with low latency, making it suitable for real-time clinical monitoring and decision support.
Real-time monitoring of patient vitals is now feasible with a cloud-native framework that seamlessly integrates diverse wearables without vendor lock-in.
Continuous physiological monitoring using consumer-grade wearables offers a transformative opportunity for clinical care and research, yet integration remains hindered by device heterogeneity, proprietary data formats, and strict regulatory requirements. We present an event-driven, cloud-native system designed to ingest, normalize, and analyze high-frequency vital signs from wearables at scale and without vendor lock-in. The system design proposes a multi-layered microservice architecture using cluster orchestration. Data acquisition is handled via a cross-platform mobile application that leverages native health frameworks, ensuring compatibility across fragmented device ecosystems. To address interoperability, we implement an event-driven transformation pipeline using stream processing engines and specialized services to map raw measurements to the FHIR standard for medical interoperability. Our novel dependency-aware FHIR minimization scheme reduces storage overhead while maintaining lossless resource reconstruction. Furthermore, the platform integrates a modular data analytics and machine learning layer based on a medallion lakehouse architecture, supporting the full machine learning lifecycle from real-time stream processing to model serving. Performance evaluation demonstrates that the ingestion pipeline sustains 50 full ingestion requests per second with median response times under 8 ms, satisfying the low-latency requirements for real-time patient monitoring. Our open-source implementation adheres to regulatory compliance standards through role-based access control and secure service-to-service communication, providing a robust foundation for deploying wearable-based monitoring in institutional healthcare settings for clinical decision support and research workloads.
