Unsupervised Cross-subject Adaptation for Predicting Human Locomotion Intent

This is the implementation of Unsupervised Cross-subject Adaptation for Predicting Human Locomotion Intent in Pytorch.

Getting Started

Installation

pip install -r requirements.txt

Download dataset

If you can use google drive, you don't need to download the data manually and just run the code shown below.

If you cannot use google drive, you need to download the dataset and checkpoint from the link below:

https://alumniubcca-my.sharepoint.com/:f:/g/personal/kuangen_zhang_alumni_ubc_ca/EmYydTnluklBn17qVXnSIWoBvBq0arhyATCaVlYXVs4PhA?e=evB0s7

Test

python code/main_MCD.py --eval_only True

Train

python code/main_MCD.py

Contact

For more related works and codes, please view my homepage: https://sites.google.com/view/kuangenzhang

Further information please contact Kuangen Zhang ([email protected]).

Citation

If you find our work useful in your research, please consider citing:

@ARTICLE{zhang_unsupervised_2020,
author={K. {Zhang} and J. {Wang} and C. W. {De Silva} and C. {Fu}},
journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering},
title={Unsupervised Cross-subject Adaptation for Predicting Human Locomotion Intent},
year={2020},
volume={},
number={},
pages={1-1},
keywords={Cross-subject adaptation;unsupervised learning;human intent classification;wearable robots},
doi={10.1109/TNSRE.2020.2966749},
ISSN={1558-0210},
month={},}

Acknowledgement

We acknowledge that we borrow the code from MCD_DA heavily.

Reference

• K. Saito, K. Watanabe, Y. Ushiku, and T. Harada, “Maximum classifier discrepancy for unsupervised domain adaptation,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA: IEEE, Jun. 2018, pp. 3723–3732.