emg2pose

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A dataset of Surface electromyography (sEMG) recordings paired with ground-truth, motion-capture recordings of the hands. Data loading, baseline model training, and baseline model evaluation code are provided.

Data

The entire dataset has $25,253$ HDF5 files, each consisting of time-aligned, 2kHz sEMG and joint angles for a single hand in a single stage. Each stage is ~1 minute. There are $193$ participants, spanning $370$ hours and $29$ stages. emg2pose.data.Emg2PoseSessionData offers a programmatic read-only interface into the HDF5 session files.

The full dataset statistics are as follows:

The metadata.csv file includes the following information for each HDF5 file:

Column	Description
user	Anonymized user ID
session	Recording session (there are multiple stages per recording session)
stage	Name of stage
side	Hand side (left or right)
moving_hand	Whether the hand is prompted to move during the stage
held_out_user	Whether the user is held out from the training set
held_out_stage	Whether the stage is held out from the training set
split	train, test, or val
generalization	Type of generalization; across user (user), stage (stage), or across user and stage (user_stage)

Setup

Environment and Dependencies

# Clone the repo, setup environment, and install local package
# NOTE: the facebookresearch github repo will be available for the camera-ready version
git clone git@github.com:facebookresearch/emg2pose.git ~/emg2pose
cd ~/emg2pose
conda env create -f environment.yml

# Activate the environment
conda activate emg2pose

# Install the emg2pose package
pip install -e .

# Install the UmeTrack package (for forward kinematics and mesh skinning)
pip install -e emg2pose/UmeTrack

Getting Started (Small, Sanity-Check Dataset)

The full dataset is $431$ GiB -- which can be cumbersome for a quick start. As a solution, we also host a smaller (~ $600$ MiB) version of the dataset which can be downloaded and used to run a sanity-check version of the train and eval logic.

(Optional) Download Just the Metadata CSV (5 MiB)

The emg2pose_metadata.csv file described above can be downloaded on its own using the following endpoint.

NOTE: this metadata file is also included in each of the dataset downloads

# Download (just) the metadata.csv file to ~/emg2pose_metadata.csv
cd ~ && curl https://fb-ctrl-oss.s3.amazonaws.com/emg2pose/emg2pose_metadata.csv -o emg2pose_metadata.csv

Download a Smaller Version of the Dataset (~600 MiB)

# Download a mini (600 MiB) version of the dataset
cd ~ && curl "https://fb-ctrl-oss.s3.amazonaws.com/emg2pose/emg2pose_dataset_mini.tar" -o emg2pose_dataset_mini.tar

# Unpack the tar to ~/emg2pose_dataset_mini
tar -xvf emg2pose_dataset_mini.tar

Sanity Check Train / Eval

To run a sanity-check training workflow over the small, sanity-check version of the dataset, please use the following command.

This runs training for the tracking_vemg2pose experiment for $5$ epochs as a sanity check. It also runs evaluation on the validation and test splits -- again as a sanity check.

python -m emg2pose.train \
train=True \
eval=True \
experiment=tracking_vemg2pose \
trainer.max_epochs=5 \
data_split=mini_split \
data_location="${HOME}/emg2pose_dataset_mini"

Getting Started (Full Dataset)

Above, we provided instructions for working with a smaller version of the dataset as a means of sanity checking the main entrypoint (train.py). Here, we show how to get started with the whole dataset.

Download the Full Dataset (431 GiB)

# Download the full (431 GiB) version of the dataset, extract to ~/emg2pose_dataset
cd ~ && curl https://fb-ctrl-oss.s3.amazonaws.com/emg2pose/emg2pose_dataset.tar -o emg2pose_dataset.tar

# Unpack the tar to ~/emg2pose_dataset
tar -xvf emg2pose_dataset.tar

Train on the Full Dataset

To launch an example, full training run for the vemg2pose (tracking) setting, use the following:

python -m emg2pose.train \
train=True \
eval=True \
experiment=tracking_vemg2pose \
data_location="${HOME}/emg2pose_dataset"

The experiment CLI option supports the following experiments (see config/experiment files):

• tracking_vemg2pose
• regression_vemg2pose
• regression_neuropose

Downloading Pre-trained Checkpoints

We provide pre-trained checkpoints (as .ckpt files) for the following:

1. vemg2pose (tracking, regression settings)
2. neuropose (regression setting)

To download and unpack these checkpoints, run the following.

# Download checkpoints
cd ~ && curl "https://fb-ctrl-oss.s3.amazonaws.com/emg2pose/emg2pose_model_checkpoints.tar.gz" -o emg2pose_model_checkpoints.tar.gz

# Unpack to ~/emg2pose_model_checkpoints
tar -xvzf emg2pose_model_checkpoints.tar.gz

Evaluation / Testing

To run basic evaluation for the validation / test splits, use the following:

Note that the experiment option to this script should match the checkpoint's experiment.

# Run train.py with train=False to isolate basic evaluation logic
python -m emg2pose.train \
train=False \
eval=True \
data_location="${HOME}/emg2pose_dataset" \
experiment=tracking_vemg2pose \
checkpoint="${HOME}/emg2pose_model_checkpoints/tracking_vemg2pose.ckpt"

To run analyses for different modes of generalization and to generate a .csv file with results, use the following script.

Note that the experiment option to this script should match the checkpoint's experiment.

python -m emg2pose.test_analysis \
data_location="${HOME}/emg2pose_dataset" \
experiment=tracking_vemg2pose \
checkpoint="${HOME}/emg2pose_model_checkpoints/tracking_vemg2pose.ckpt"

Notebook and Visualization

Check out the Jupyter Notebook in notebooks/getting_started.ipynb for a brief walkthrough of data loading, inference, and data visualization.

License

emg2pose is CC-BY-NC-SA-4.0 licensed, as found in the LICENSE file.

emg2pose is also licensed subject to the licenses of its code dependencies.

UmeTrack is licensed under Attribution-NonCommercial 4.0 International, as found in the emg2pose/UmeTrack/LICENSE and GitHub.

Citing emg2pose

@inproceedings{salteremg2pose,
  title={emg2pose: A Large and Diverse Benchmark for Surface Electromyographic Hand Pose Estimation},
  author={Salter, Sasha and Warren, Richard and Schlager, Collin and Spurr, Adrian and Han, Shangchen and Bhasin, Rohin and Cai, Yujun and Walkington, Peter and Bolarinwa, Anuoluwapo and Wang, Robert and others},
  booktitle={The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track}
}