> For the complete documentation index, see [llms.txt](https://maha-strategies-llc.gitbook.io/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://maha-strategies-llc.gitbook.io/docs/core-concepts/telemetry-and-data-pipelines.md). # Telemetry & Data Pipelines To enforce the Biological Sovereignty Loop, Maha OS must maintain a continuous, low-latency understanding of the user's physiological state. This section details how the Sovereign Client ingests health data and manages local resources without violating edge-compute principles. *** ### 1. Health Data Ingestion The local node (`com.maha.os`) acts as an aggregator for the user's existing wearable and biometric hardware. It does not generate its own raw health data; instead, it interfaces securely with native OS health repositories. * **Google Health Connect & Apple HealthKit:** The client requests read-only access to specific, high-signal biometric streams. By routing through native health APIs, Maha OS maintains interoperability with devices like Oura, Dexcom, and Garmin without requiring proprietary SDKs. * **Key Extracted Metrics:** \* **Resting Heart Rate (RHR) & Heart Rate Variability (HRV):** Used to gauge real-time sympathetic nervous system arousal and metabolic fatigue. * **Decision Velocity:** A proprietary heuristic calculated locally based on interface interaction speed and task-switching frequency. * **Systemic Readiness:** An aggregate percentage reflecting the user's capacity to handle cognitive load before algorithmic extraction takes hold. *** ### 2. Adaptive Background Polling Continuous biometric ingestion and persistent server connections are inherently resource-intensive on Android devices. To prevent severe battery drain and OS-level process termination, Maha OS employs an adaptive polling architecture. * **Baseline State:** When the user is in a parasympathetic state (e.g., stable RHR, low device interaction), the background polling degrades to a low-frequency cycle utilizing the Android WorkManager API for non-critical updates. * **Active Escalation:** If the client detects a rapid increase in interaction velocity or a sustained spike in physiological stress markers, the polling rate automatically accelerates. The application seamlessly transitions to a persistent WebSocket connection to the Agentic Core, preparing for a potential zero-latency kinetic intervention. * **Resource Guardrails:** If the device battery drops below a critical threshold or thermal throttling is detected (often a risk with continuous edge compute), the system temporarily suspends heavy edge tasks, such as optical food scanning, and relies solely on baseline biometric heuristics. --- # 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://maha-strategies-llc.gitbook.io/docs/core-concepts/telemetry-and-data-pipelines.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.