An Event-Driven Cloud-Native Wearable Analytics Framework for Real-Time Clinical Workloads

An Event-Driven Cloud-Native Wearable Analytics Framework for Real-Time Clinical Workloads is a scalable, open-source system designed to ingest, normalize, and analyze high-frequency vital signs from consumer-grade wearables without vendor lock-in. It addresses integration challenges by using a multi-layered microservice architecture that decouples ingestion, interoperability, and AI-inference workloads via cluster orchestration.

The framework leverages native health OS APIs (Apple HealthKit and Google Health Connect) to acquire data, mapping heterogeneous device payloads to the FHIR standard for seamless interoperability with existing hospital information systems. Key capabilities include:

• Event-Driven Processing: Utilizes a distributed event-streaming backbone to handle variable workloads and bursty data synchronization with low latency.
• Real-Time Inference: Supports immediate clinical decision support through real-time stream processing and machine learning workloads.
• Regulatory Compliance: Ensures privacy and security through role-based access control, secure service-to-service communication, and CI/CD pipelines for continuous security scanning.
• Scalable Storage: Implements a medallion lakehouse architecture to separate high-volume time-series data from relational metadata, supporting both real-time analytics and batch research processing.

Performance evaluations demonstrate that the ingestion pipeline can sustain 50 full ingestion requests per second with median response times under 8 ms, meeting the low-latency requirements for real-time patient monitoring.

This paper presents an event-driven, cloud-native framework for continuous wearable-based physiological monitoring, targeting the ingestion, normalization, and real-time analysis of high-frequency vital-sign data from consumer-grade devices. It addresses a central systems problem in clinical wearable adoption: devices produce heterogeneous, proprietary, and often irregular data streams that are difficult to integrate into clinically useful workloads, while also operating under strict privacy, auditability, and regulatory constraints. The proposed architecture is oriented toward streaming processing at scale, where raw wearable signals are transformed into standardized, analyzable event streams suitable for downstream clinical monitoring, alerting, and research analytics.

A key contribution is a scalable, decoupled pipeline design that separates device-specific ingestion from normalization, event processing, storage, and analytics. By treating incoming physiological measurements as events, the framework can accommodate diverse wearables and data formats without tightly coupling each device to the downstream clinical logic. This design is especially relevant for real-time clinical workloads, where latency, reliability, and data governance matter as much as analytical capability. The paper’s insight is that wearable clinical systems are not only data-science problems, but also integration, interoperability, and compliance problems, and that a cloud-native event-driven architecture can make the system more maintainable, extensible, and suitable for regulated environments.

The work matters because it provides a practical blueprint for moving consumer wearables from isolated data sources into operationally useful clinical infrastructure. If successfully applied, such a framework could support remote patient monitoring, longitudinal research studies, early-warning analytics, and continuous physiological surveillance without requiring bespoke point-to-point integrations for each device vendor. More broadly, it highlights how architectural choices—standardized event models, scalable stream processing, and regulatory-aware data handling—can reduce the barriers that currently prevent large-scale clinical adoption of wearable sensor data.