> For the complete documentation index, see [llms.txt](https://docs.thryve.health/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.thryve.health/thryve-analytics-platform/overview.md). # Overview The Thryve Health Analytics Platform processes wearable sensor data into health metrics following clinical and scientific best practices in near real-time. Our analytics engine transforms data from hundreds of data signals into standardized health insights suitable for healthcare applications, research, and population health monitoring. All analytics are computed and available within milliseconds of data ingestion, enabling immediate access to health insights. Algorithms are based on established clinical frameworks and peer-reviewed research, ensuring that metrics meet healthcare application requirements. ### Core Analytics Capabilities #### Real-Time Processing Analytics are calculated continuously as new data arrives, rather than through batch processing. This enables immediate health insights and supports applications requiring real-time health monitoring. #### Scientific backing The analytics platform is built on our roots in biosignal analysis and our scientific approach given Thryve was built as a spin-off of renowned Fraunhofer Institute. Where applicable we build our algorithms on top of established clinical frameworks adapted for continuous wearable data monitoring and are actively involved in research projects all around Europe. #### Data Harmonization Our data harmonization efforts go beyond storing and providing data from different kinds of data sources in a unified data format and API. With our extensive analytics suit we harmonize core health metrics across different wearable manufacturers and data sources. This solves the fundamental challenge of inconsistent metric definitions (e.g. for sleep data) between devices and enables longitudinal health tracking regardless of device changes. #### Multi-Device/Source Data Fusion Advanced algorithms handle data from multiple simultaneous sources: * **Overlap Detection**: Identifies and resolves conflicts when multiple devices record the same timeframe * **Priority-Based Selection**: Hierarchical data selection when conflicts arise * **Data Deduplication**: Prevents double-counting across connected devices Data priority is determined based on information on recording device and context. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.thryve.health/thryve-analytics-platform/overview.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.