Scalibur

BLE body composition scale monitor for Raspberry Pi. Reads weight and impedance data from a GoodPharm TY5108 (BLE name "tzc") scale and displays it on a web dashboard.

Features

• Continuous BLE scanning for scale advertisements
• Body composition calculation using standard BIA formulas (body fat %, muscle mass, BMR, etc.)
• SQLite database for persistent storage
• Web dashboard with weight charts and measurement history

Requirements

• Raspberry Pi (or any Linux system with Bluetooth LE)
• Python 3.11+
• uv package manager
• GoodPharm TY5108 body composition scale (BLE name: "tzc")

Installation

1. Clone the repository

git clone https://github.com/YOUR_USERNAME/scalibur.git
cd scalibur

2. Install dependencies

uv sync

3. Configure your profile

Copy the example config and edit with your details:

cp config.example.py config.py

Edit config.py with your height, age, and gender for accurate body composition calculations.

4. Run manually (for testing)

# Terminal 1: Start the BLE scanner
uv run python scanner.py

# Terminal 2: Start the dashboard
uv run python dashboard.py

The dashboard will be available at http://localhost:5000

Raspberry Pi Deployment

1. Deploy to your Pi

# Set your Pi's hostname and username
PI_USER=pi ./deploy.sh raspberrypi.local

2. Configure systemd services

Before the first deployment, edit the service files to replace YOUR_USER with your Pi username:

# In systemd/scalibur-scanner.service and scalibur-dashboard.service
# Replace YOUR_USER with your actual username (e.g., pi)

The deploy script will install and enable the systemd services on first run.

3. Access the dashboard

Open http://raspberrypi.local:5000 in your browser.

Usage

1. Stand on the scale and wait for the measurement to complete
2. The scanner detects the BLE advertisement and stores the reading
3. View your data on the web dashboard

Architecture

scanner.py (systemd daemon)
    │
    │ BLE advertisement
    │ └─ filter by name="tzc"
    │ └─ wait for measurement complete
    │ └─ debounce (30s cooldown)
    │
    ▼
measurements.db (SQLite)
    │
    ▼
dashboard.py (Flask on :5000)
    │
    └─ Latest reading + weight chart + history

Security Notes

• The web dashboard has no authentication and listens on all interfaces by default
• Only expose on trusted networks or add a reverse proxy with authentication
• The database contains personal health data - keep backups secure

Protocol

Device: GoodPharm TY5108 body composition scale BLE Name: tzc Manufacturer ID: varies (ignored; filter by device name only)

Advertisement Packet Format

Raw packets are stored as {manufacturer_id:04x}:{manufacturer_data.hex()}.

manufacturer_data bytes:

Bytes	Description
0-1	Weight (big-endian, divide by 10 for kg)
2-3	Impedance (big-endian, divide by 10 for ohms; 0 = not measured)
4-5	User ID (big-endian)
6	Status: 0x20 = weight complete, 0x21 = complete with impedance
7+	MAC address (ignored)

Example: 74c0:03380000000220fe98000c91d8

• Weight: 0x0338 = 824 → 82.4 kg
• Impedance: 0x0000 = 0 → not measured yet
• User ID: 0x0002 = 2
• Status: 0x20 = weight complete (no impedance)

Body Composition

Body composition is calculated using standard BIA (Bioelectrical Impedance Analysis) formulas in decode.py:

• Lean Body Mass (LBM) from height²/impedance
• Body fat % = (weight - LBM) / weight
• BMR using Mifflin-St Jeor equation
• BMI, muscle mass, water %, bone mass estimates

Database Schema

CREATE TABLE measurements (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    timestamp DATETIME DEFAULT CURRENT_TIMESTAMP,
    weight_kg REAL NOT NULL,
    impedance_raw INTEGER,
    impedance_ohm REAL,
    body_fat_pct REAL,
    fat_mass_kg REAL,
    lean_mass_kg REAL,
    body_water_pct REAL,
    muscle_mass_kg REAL,
    bone_mass_kg REAL,
    bmr_kcal INTEGER,
    bmi REAL
);

CREATE TABLE raw_packets (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    timestamp DATETIME DEFAULT CURRENT_TIMESTAMP,
    packet_hex TEXT NOT NULL
);

License

GPL-3.0 - see LICENSE