System Architecture

2. System Architecture

2.1 Design Principles

The Myome framework is built on five foundational design principles that distinguish it from existing health tracking solutions:

1. Local-First Privacy - All health data is stored locally on user-controlled devices by default, with optional encrypted cloud synchronization. Users maintain complete ownership and control over their data, with granular permissions for sharing with healthcare providers, researchers, or family members.

2. Sensor Agnosticism - The system abstracts device-specific implementations behind a unified API, enabling seamless integration of new sensors and testing services without architectural changes. This ensures longevity as technology evolves.

3. Clinical Validity - All algorithms, predictive models, and health insights are grounded in peer-reviewed clinical research. The system provides citations and confidence intervals for all recommendations.

4. Minimal Burden - The target is 3-5 minutes of daily active measurement time, with the majority of data collection occurring passively through wearable sensors and environmental monitors.

5. Open Source Transparency - All code, algorithms, and data schemas are open source, enabling community validation, extension, and trust through transparency.

2.2 Data Pipeline Architecture

The Myome data pipeline consists of five stages: ingestion, normalization, storage, analysis, and presentation. This architecture ensures data quality while maintaining real-time responsiveness for time-sensitive health insights.

Stage 1: Ingestion - Data enters the system through four primary channels:

• Wearable devices - Continuous streaming data via Bluetooth LE or proprietary APIs (Oura Ring, Apple Watch, Garmin, Whoop, Levels CGM)
• Laboratory tests - Batch uploads of structured results from genomic testing (23andMe, Nebula Genomics), microbiome analysis (Thorne, Viome), blood panels (InsideTracker, Siphox)
• Environmental sensors - Periodic polling of air quality monitors, weather APIs, and location-based exposure databases
• Manual entry - User-reported symptoms, medications, meals, and subjective wellness measures

Stage 2: Normalization - Raw data undergoes transformation to standard units and reference ranges. This includes:

• Unit conversion (mg/dL ↔ mmol/L for glucose, different heart rate measurement protocols)
• Calibration against reference standards (wearable sensor drift correction)
• Missing data imputation using forward-fill or interpolation for sensor dropouts
• Outlier detection and flagging (physiologically implausible values)

Stage 3: Storage - Normalized data is persisted in a hybrid storage architecture:

• Time-series database (InfluxDB or TimescaleDB) for high-frequency physiological data (heart rate, glucose, activity)
• Document store (SQLite with JSON columns) for semi-structured lab results and genomic data
• Object storage for medical images and large files (DICOM, PDF reports)

Stage 4: Analysis - Stored data feeds into analytical engines that compute:

• Cross-ome correlations (e.g., sleep quality ↔ glucose variability)
• Trend detection and change-point analysis
• Predictive models for health trajectory forecasting
• Anomaly detection for early warning of pathological changes

Stage 5: Presentation - Analyzed results are rendered through multiple interfaces:

• Personal health dashboard (web and mobile applications)
• Physician summary reports (PDF generation with key insights)
• Data export for EHR integration (HL7 FHIR format)
• API access for third-party applications

2.3 Storage and Privacy Model

Health data is among the most sensitive personal information, requiring robust privacy protections. Myome implements a local-first architecture where all data resides primarily on user-controlled devices (smartphones, personal computers) with optional encrypted synchronization to user-specified cloud storage.

The storage schema is designed for longitudinal data spanning decades. A typical user accumulates approximately 50 MB per year of high-frequency sensor data, plus 10-100 MB annually from lab tests and imaging. Over a lifetime, this totals ~5 GB—easily manageable on modern devices while supporting rich analytical queries.