Private Health

This project aims to create the necessary building blocks in order to allow collect and analyze one's personal health data in a privacy-oriented manner. Instead of closed-source apps and dubious clouds, it should use a FLOSS stack.

Architecture

This architecture leans on proven, reusable components wherever possible. Refer to the diagram below for an overview: it highlights mature apps in green, partially solved areas in orange, and gaps that require mostly new development in red.

At the moment, the HealthConnect API is remains a pragmatic choice for integration and synchronisation. It lives on the user’s phone, encrypts data, and speaks schemas for dozens of vendors. That buys time for a gentle migration: you can keep your current wearable, pull data into a local store, and steadily replace proprietary parts as FLOSS apps/adapters mature.

However, alternative solutions to the HealthConnect API are more than welcome.

flowchart LR
  scale(("bathroom<br>scale"))
  A["openscale"]
  B["openscale sync"]
  hc["HealthConnect API<br>(or similar)"]

  fitness_tracker(("fitness<br>tracker"))
  gb["Gadgetbridge"]
  gb_sync["(Gadgetbridge sync?)"]

  nc[("Nextcloud")]

  bpm(("blood<br>pressure<br> monitor"))
  --> bpm_app["BPM app"]
  --> hc

  scale-->A
  --> B
  --> hc
  
  fitness_tracker
  --> gb
  --> gb_sync
  --> hc

  hc --> nha["Nextcloud (health) app"]

  nha --> nc

  nc --> cA["Web UI<br>(Dashboard, statistics)"]
  nc --> cB["mobile devices"]
  nc --> cC["...other clients..."]

  classDef green fill:#8fb935,stroke:#333;
  classDef yellow fill:#e6e22e,stroke:#333;
  classDef orange fill:#e09c3b,stroke:#333,stroke-width:4px;
  classDef red fill:#e64747,stroke:#333,stroke-width:4px;
  class A,B,gb,hc green;
  class bpm_app,gb_sync,cA,nc orange;
  class nha red;