AtGCN

A lightweight Spatiotemporal GCN for Ataxia Detection. Official implementation for AtGCN, accepted to AIME-2025 as a full length paper + talk.

To make our pipeline fuly reproducible, we provide all the code used and also instructions on how to reproduce.

Basic environment setup

You can install the dependencies with these commands (We have used python 3.9.19)

pip install -r requirements.txt

OR if you use conda, then first run

conda create -n ataxia-sync python=3.9.19

Data Preparation - V1

Please download the first dataset we have used from here; if you want to use your own dataset, the same instructions as below follow.

1. First create a folder named data/ in the root directory of the project.
2. Use the notebooks, FRCNN.ipynb, SORT.ipynb and finally OpenPose.ipynb in this order from the src/preprocess/ directory on Google Colab to get keypoints of all patients, store these inside the data/ directory in a folder named final_keypoints/. (Note: this step takes ~4 hours for the 149 videos we had using a T4 GPU)
3. Create npy files by running

python3 src/preprocess/create_npy.py 1

4. Using these keypoints we can now extract non-overlapping gait cycles. To do this, run the gait_extractor.py script from the src/preprocess/ directory. This will create a folder named non_overlapping_gait_cycles/ inside the data/ directory (or gait_cycles/ if you want to extract overlapping gait cycles, we use non-overlapping gait-cycles in the paper and have not tested the overlapping case). Run with -

python src/preprocess/gait_extractor.py 1

This should print something like -

Minimum number of peaks: xx at yy
Average number of peaks: xy.z
Maximum number of peaks: zz
Minimum number of gait cycles: xx at yy
Average number of gait cycles: xy.z
Maximum number of gait cycles: zz
Number of gait cycles: abc
Minimum length of gait cycle: xx at yy
Average length of gait cycle: xy.z
Maximum length of gait cycle: zz at yy

5. Finally, we create CSV files from which we can quickly retrieve data and use it in our training loop, you can use create_csvs.py from the src/preprocess/ directory to create these CSV files, in our format.

This will create the folder with all the files and extracted Gait Cycles (overlapping and non-overlapping, can do both).

Data Preparation - V2

We have another dataset of 40 videos, which can be downloaded from here / paper. This already has extracted keypoints. NOTE: according to current preprocessing, you can only use one dataset at a time, as the CSV files and folders are overwritten. To switch between datasets, just rename the folders accordingly.

1. Use the create_npy.py from the src/preprocess/ directory, which will store the keypoints in .npy files and create a V2.csv file which contains a mapping of original files to assigned IDs and their labels, we also add a center coordinate to the keypoints (last keypoint).

python3 src/preprocess/create_npy.py 2

2. Then use the gait_extractor.py to extract non-overlapping gait cycles (will be saved similarly to the first dataset). Run with

python src/preprocess/gait_extractor.py 2

3. Finally, create the CSV files using create_csvs.py from the src/preprocess/ directory.

A few notes on this dataset:

• The dataset available online is the augmented version, we deaugment and use it in our case.
• The authors report 31 augmentations + 1 original, however, their implementation and the trend in the dataset observed by plotting shows original + 32 augmentations, so we divide the dataset by 33 and use the first [:x//33] frames per video.

Model checkpoints

We have already uploaded the required checkpoints inside ckpts/. These were taken from -

• STGCN checkpoints : here.
• GaitGraph checkpoints : here.

Training

To train a model you can use the runner.sh with sbatch on an HPC or with bash on your local machine.

bash runner.sh

You can also check the flags using

python src/trainer.py --help # or -h

To reproduce our results, you can run the runner.sh as is. A full 10-fold CV run takes about 40 minutes on a single V100 GPU.

Analysis

You can also reproduce all the plots and tables in the paper using src/analyze.py, just uncomment all the lines after if __name__ == "__main__", and run,

python3 src/analyze.py

Main results

Please create an issue if you need some functionality or the code doesn't work as intended. Thank you!

Citation

@InProceedings{10.1007/978-3-031-95838-0_3,
      author="Bania, Karan 
      and Verlekar, Tanmay Tulsidas",
      editor="Bellazzi, Riccardo
      and Juarez Herrero, Jos{\'e} Manuel
      and Sacchi, Lucia
      and Zupan, Bla{\v{z}}",
      title="AtGCN: A Lightweight Graph Convolutional Network for Ataxic Gait Detection",
      booktitle="Artificial Intelligence in Medicine",
      year="2025",
      publisher="Springer Nature Switzerland",
      address="Cham",
      pages="22--32",
      isbn="978-3-031-95838-0"
}