Open-source wearable analytics framework enables real-time clinical monitoring at scale

A team of researchers led by Elias Grünewald at Charité – Universitätsmedizin Berlin has released an open-source, event-driven cloud-native framework designed to bring consumer wearables into clinical practice. The system tackles the core barriers to wearable adoption in healthcare: device fragmentation, proprietary data formats, and strict regulatory constraints. It uses a multi-layered microservice architecture with cluster orchestration to ingest high-frequency vital signs from devices like smartwatches, normalized through a cross-platform mobile app that leverages native health frameworks. To ensure interoperability, the pipeline maps raw measurements to FHIR (Fast Healthcare Interoperability Resources), the global standard for medical data exchange, via stream processing engines and specialized transformation services.

Performance testing shows the ingestion pipeline handles 50 full requests per second with median response times under 8 milliseconds, meeting the low-latency needs of real-time patient monitoring. A novel dependency-aware FHIR minimization scheme cuts storage overhead while preserving lossless resource reconstruction, and a modular analytics layer built on a medallion lakehouse architecture supports the full ML lifecycle—from stream processing to model serving. The platform includes role-based access control and secure service-to-service communication for regulatory compliance. Published on arXiv (2608.11402), it provides a robust foundation for deploying wearable-based monitoring in hospitals, enabling clinical decision support and large-scale research workloads without vendor lock-in.