Yoga Classifier

Yoga has become increasingly popular over the years, providing many health benefits. In our project, we aim to create a Multilayer Perceptron model that can identify yoga poses. We use BlazePose 3D to identify the keypoints and calculate the normalized distances. Our testing accuracy is 100%, a very good result. Our recommendation for future work is to use a dataset with a higher variety of poses and return feedback about the performed exercises.

The report is in the report folder.

Dataset

How tu run the code

Create an environment and activate it:

conda create --name ci-yoga python=3.9
conda activate ci-yoga
pip install -r requirements.txt

The main code is in the yoga folder:

• To process data yoga/process_data:
  • make_dataset.py: to transform images into datasets of key points
• Models yoga/models:
  • build_features.py : to transform the pose keypoints dataset into final_dataset.csv
  • train_model.py: train three classifiers and save performance information in test_sats.csv
  • analysis.py: useful plots to compare the models