# radarODE: An ODE-Embedded Deep Learning Model for Contactless ECG Reconstruction from Millimeter-Wave Radar

arXiv: https://arxiv.org/abs/2408.01672 · HTML: https://mlsanity.com/paper/2408.01672/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 79 references that are in the map.
- First posted on arXiv 3 Aug 2024.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [A Survey of Transformers](https://mlsanity.com/paper/2106.04554/) (2021)
- [Why ResNet Works? Residuals Generalize](https://mlsanity.com/paper/1904.01367/) (2019)
- [The Euclidean Space is Evil: Hyperbolic Attribute Editing for Few-shot Image Generation](https://mlsanity.com/paper/2211.12347/) (2022)
- [Achelous: A Fast Unified Water-surface Panoptic Perception Framework based on Fusion of Monocular Camera and 4D mmWave Radar](https://mlsanity.com/paper/2307.07102/) (2023)
- [Deep Learning for Radio-based Human Sensing: Recent Advances and Future Directions](https://mlsanity.com/paper/2010.12717/) (2020)
- [Contactless Electrocardiogram Monitoring with Millimeter Wave Radar](https://mlsanity.com/paper/2112.06639/) (2021)
- [Anchor Data Augmentation](https://mlsanity.com/paper/2311.06965/) (2023)
- [Intelligent Metasurface Imager and Recognizer](https://mlsanity.com/paper/1910.00497/) (2019)
- [Vital Sign Monitoring in Dynamic Environment via mmWave Radar and Camera Fusion](https://mlsanity.com/paper/2304.11057/) (2023)

## Built on by (most cited first, 7 of 7)

- [radarODE-MTL: A Multi-Task Learning Framework with Eccentric Gradient Alignment for Robust Radar-Based ECG Reconstruction](https://mlsanity.com/paper/2410.08656/) (2024)
- [From High-SNR Radar Signal to ECG: A Transfer Learning Model with Cardio-Focusing Algorithm for Scenarios with Limited Data](https://mlsanity.com/paper/2506.19358/) (2025)
- [Recover from Horcrux: A Spectrogram Augmentation Method for Cardiac Feature Monitoring from Radar Signal Components](https://mlsanity.com/paper/2503.19649/) (2025)
- [Physics-Constrained Deep Learning Model for Contactless Blood Pressure Monitoring from Triaxial Bodyseismography](https://mlsanity.com/paper/2608.23562/) (2026)
- [LifWavNet: Lifting Wavelet-based Network for Non-contact ECG Reconstruction from Radar](https://mlsanity.com/paper/2510.27692/) (2025)
- [Learning to Assess Heartbeat Observability for mmWave Heart-Rate Sensing](https://mlsanity.com/paper/2610.03570/) (2026)
- [Kapture: Capturing Cardiac Dynamics with Koopman-Governed Learning for Efficient Radar-Based Electrocardiogram Recovery](https://mlsanity.com/paper/2610.04955/) (2026)

## Abstract (arXiv)

Radar-based contactless cardiac monitoring has become a popular research direction recently, but the fine-grained electrocardiogram (ECG) signal is still hard to reconstruct from millimeter-wave radar signal. The key obstacle is to decouple the cardiac activities in the electrical domain (i.e., ECG) from that in the mechanical domain (i.e., heartbeat), and most existing research only uses pure data-driven methods to map such domain transformation as a black box. Therefore, this work first proposes a signal model for domain transformation, and then a novel deep learning framework called radarODE is designed to fuse the temporal and morphological features extracted from radar signals and generate ECG. In addition, ordinary differential equations are embedded in radarODE as a decoder to provide morphological prior, helping the convergence of the model training and improving the robustness under body movements. After being validated on the dataset, the proposed radarODE achieves better performance compared with the benchmark in terms of missed detection rate, root mean square error, Pearson correlation coefficient with the improvement of 9%, 16% and 19%, respectively. The validation results imply that radarODE is capable of recovering ECG signals from radar signals with high fidelity and can be potentially implemented in real-life scenarios.