radarODE-MTL

radarODE-MTL is an open-source library built on PyTorch and Multi-Task Learning (MTL) framework LibMTL.

Code for Paper:

1. radarODE: An ODE-embedded deep learning model for contactless ECG reconstruction from millimeter-wave radar
2. radarODE-MTL: A Multi-Task Learning Framework with Eccentric Gradient Alignment for Robust Radar-Based ECG Reconstruction

Presentations for:

1. radarODE
2. radarODE-MTL
3. Introduction of popular MTL optimization stratigies

🥳 Any problem please send them in Issues or Email 📧.

Citation

If you find our work helpful for your research, please cite our paper:

@article{zhang2024radarODE,
  title={{radarODE: An ODE-embedded deep learning model for contactless ECG reconstruction from millimeter-wave radar}}, 
  author={Yuanyuan Zhang and Runwei Guan and Lingxiao Li and Rui Yang and Yutao Yue and Eng Gee Lim},
  year={2024},
  journal={arXiv preprint arXiv:2408.01672 [eess]},
  month={Aug.},
}
@article{zhang2024radarODE-MTL,
  title={radarODE-MTL: A Multi-task learning tramework with eccentric gradient alignment for robust radar-based {ECG} reconstruction}, 
  author={Yuanyuan Zhang and Rui Yang and Yutao Yue and Eng Gee Lim},
  year={2024},
  journal={arXiv preprint arXiv:2410.08656 [eess]},
  month={Oct.},
}

Dataset Download and Preparation

Please refer to MMECG Dataset for the Dataset downloading.

The file structure is

Dataset
└───obj1_NB_1_
│   │   sst_seg_0.npy
│   │   anchor_seg_0.npy 
│   │   ecg_seg_0.npy
│   │   ...
│   ...
└───obj30_PE_91_
│   │   ...
│   │   sst_seg_215.npy
│   │   anchor_seg_215.npy 
│   │   ecg_seg_215.npy

The input size of the radarODE and radarODE-MTL are the spectrograms with size 50x71x120 (e.g., sst_seg_0.npy), with 71 for frequency and 120 for 3-sec segments. The ground truth ECG, anchor, cycle length can be fomulated as in the paper.

Run the Model

You can find the arguments and settings in:

radarODE-MTL/Projects/radarODE_plus/main.py

The model summary is in:

radarODE-MTL/Projects/radarODE_plus/nets/model.py

More details on the available MTL Architectures, Optimization Strategies and Datasets please refer to LibMTL.

Quick Introduction

The full presentation for radarODE-MTL is shown in

radarODE/Presentations/radarODE_MTL_Presentation.pdf

License

radarODE-MTL is released under the MIT license.