This repository was written to help analyze the Virginia Tech Natural Motion Dataset. The dataset contains 40 hours of unscripted human motion collected in the open world using XSens MVN Link. The dataset, metadata and more information is available through the Virginia Tech University Libraries: https://data.lib.vt.edu/articles/dataset/Virginia_Tech_Natural_Motion_Dataset/14114054/2.

• Layout
• Workflow
• Dependencies
• Conda Environment

src/                                                                                                  
    common/
    seq2seq/
    transformers                                       
    matlab/

numpy==1.18.1
h5py==2.10.0
matplotlib==3.1.3
torch==1.6.0

• Clone the repo locally
• Setup the conda environment
  • $ conda create -n vt-nmp python=3.7
• Install requirements
  • $ pip install -r requirements.txt

An Anaconda environment is used to help with development. The environment's main dependency is PyTorch, which will be installed when setting up the workflow above.

Please see the LICENSE for more details. If you use our code or models in your research, please cite our paper:

@article{geissinger2020motion,
  title={Motion inference using sparse inertial sensors, self-supervised learning, and a new dataset of unscripted human motion},
  author={Geissinger, Jack H and Asbeck, Alan T},
  journal={Sensors},
  volume={20},
  number={21},
  pages={6330},
  year={2020},
  publisher={Multidisciplinary Digital Publishing Institute}
}