📋 Table of contents
GymBro AI skal bruke IMU-sensorer og AI for å gjenkjenne treningsøvelser og telle repetisjoner automatisk! Vi skal utforske AI Pose Estimation for å vurdere treningsform og utvikle et system som gir sanntids tilbakemelding på teknikk. Prosjektet kombinerer mikrokontrollere med maskinlæring og Computer Vision for en praktisk anvendelse som hjelper folk med å trene tryggere og mer effektivt.
- Git: Ensure that git is installed on your machine. Download Git
- Python 3.12: Required for the project. Download Python
- UV: Used for managing Python environments. Install UV
- Docker (optional): For DevContainer development. Download Docker
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Clone the repository:
git clone https://github.com/CogitoNTNU/GymbroAI.git cd GymbroAI -
Install dependencies:
uv sync
- Set up pre commit (only for development):
uv run pre-commit install
To run the project, run the following command from the root directory of the project:
To build and preview the documentation site locally:
uv run mkdocs build
uv run mkdocs serveThis will build the documentation and start a local server at http://127.0.0.1:8000/ where you can browse the docs and API reference. Get the documentation according to the lastes commit on main by viewing the gh-pages branch on GitHub: https://cogitontnu.github.io/GymbroAI/.
To run the test suite, run the following command from the root directory of the project:
uv run pytest --doctest-modules --cov=src --cov-report=htmlThis project would not have been possible without the hard work and dedication of all of the contributors. Thank you for the time and effort you have put into making this project a reality.
 Dennis Jevne |
 Vittorio Avellone |
 Jens Valderhaug |
 Gustav Natvig |
 Ivan Kochura |
 Henrik Oen |
Distributed under the MIT License. See LICENSE for more information.