In today's world, a healthy lifestyle is becoming increasingly relevant, and with it, interest in sports activities is growing. However, gaining experience and knowledge in this field can be a challenging task for many people. In this context, the application of artificial intelligence (AI) in the sports sector becomes a key element of successful training, analysis, and development of sports teams and individual athletes.
This repository provides a set of tools to help you improve your technique for the following exercises: front squats, wide-arm push-ups, biceps push-ups, reverse push-ups. This intelligent assistant analyzes your technique in real time, evaluates your posture using an AI model (yolov8-pose) and gives you feedback on your form.
A counter for correctly completed sets and so-called attempts to perform the exercise correctly has also been added. This will help you better understand your exercise statistics.
This project uses a trained YOLOv8m-pose model. However, you can use the weights of 2 other trained models: YOLOv8n-pose and YOLOv8s-pose located in the models/yolo and models/yolo2 folders respectively, but these results are worse than YOLOv8m-pose.
These models are pose detection models that are trained on COCO-pose typed data. This dataset includes 17 keypoints. For clarity, below is a markup image.
More information on the dataset can be found here: COCO-Human-Pose and Ultralytics: COCO-Pose Dataset.
- Clone repository.
git clone https://github.com/KKopilka/AI-FinessTrainer.git
- Install the requirements.
pip install -r requirements.txt
- Run the script.
python manual.py <exercise_name>
- It is possible to run the project with streamlit.
streamlit run app/live.py
- If you want to run the project through docker. Documentation 👉 Deploy Streamlit using Docker.
docker build -t streamlit .
docker-compose up -d
-
Train a model for human pose estimation.
-
Integration of the model into the project, processing of key points.
-
Add exercises for major muscle groups.
-
Add a counter for approaches and attempts.
-
Run locally or through a browser (streamlit).
-
Launching via Docker.
This project is not a fully finished version, so it can still be finalized.
Here are some ideas on how to improve this project are as follows:
- Add more exercises.
- Add more statistics to the program.
- Add a web/mobile app.
- Add sound accompaniment.
- Convert the project to an .exe file.