lglab is a powerful tool to connect, retrieve, and analyze data from your fitness devices. It provides a unified interface to interact with various fitness trackers and smartwatches, allowing you to own and explore your health data.
In a world of countless fitness gadgets, each with its own app and ecosystem, lglab aims to break down the data silos. This project was born out of a desire to have a single, open-source platform to aggregate and analyze personal fitness metrics without being tied to a specific vendor's platform.
With lglab, you can:
- Download your activity data (steps, heart rate, sleep, etc.).
- Perform custom analysis and create visualizations.
- Export your data to common formats like CSV or Parquet for further use.
- Keep a local, private backup of your fitness history.
- Multi-Device Support: Connect to a growing list of fitness devices.
- Data-Centric: Fetches raw data, giving you full control.
- Extensible: Easy to add support for new devices.
- Python-based: Leverage the power of pandas, NumPy, and other data science libraries for analysis.
To get a local copy up and running, follow these simple steps.
This project requires Python 3.13+ and uv as package manager.
- Clone the repo
git clone https://github.com/kopib/lglab.git
- Navigate to the project directory
cd lglab - Install Python packages
uv install .
Here's a quick example of how to use lglab to fetch your latest activity data.
from lglab.devices import supported_devices
from lglab.client import Client
# See which devices are supported
print(supported_devices())
# Initialize a client for your device
# (This may require authentication on first run)
client = Client("YourDeviceBrand")
# Get your personal info
personal_info = client.get_personal_info()
print(personal_info.to_dataframe())For more examples, please refer to the Documentation.
Currently, lglab supports the following devices:
- Oura
We are actively working on expanding our list of supported hardware. Support for more fitness devices and smartwatches from other major brands is planned for future releases. If you would like to see a specific device supported, please open an issue!
- Add data fetching support for Oura
- Create simple analyzer for fetched data
- Create a simple web-based dashboard for visualization
- Support more devices
See the open issues for a full list of proposed features (and known issues).
Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE.txt for more information.