# Contactless Electrocardiogram Monitoring with Millimeter Wave Radar

arXiv: https://arxiv.org/abs/2112.06639 · HTML: https://mlsanity.com/paper/2112.06639/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2021.

## Built on by (most cited first, 9 of 9)

- [Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2312.04861/) (2023)
- [radarODE: An ODE-Embedded Deep Learning Model for Contactless ECG Reconstruction from Millimeter-Wave Radar](https://mlsanity.com/paper/2408.01672/) (2024)
- [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)
- [RFPose-OT: RF-Based 3D Human Pose Estimation via Optimal Transport Theory](https://mlsanity.com/paper/2301.13013/) (2022)
- [Deep learning for radar data exploitation of autonomous vehicle](https://mlsanity.com/paper/2203.08038/) (2022)
- [UWB Radar-based Heart Rate Monitoring: A Transfer Learning Approach](https://mlsanity.com/paper/2507.14195/) (2025)
- [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)

## Abstract (arXiv)

The electrocardiogram (ECG) has always been an important biomedical test to diagnose cardiovascular diseases. Current approaches for ECG monitoring are based on body attached electrodes leading to uncomfortable user experience. Therefore, contactless ECG monitoring has drawn tremendous attention, which however remains unsolved. In fact, cardiac electrical-mechanical activities are coupling in a well-coordinated pattern. In this paper, we achieve contactless ECG monitoring by breaking the boundary between the cardiac mechanical and electrical activity. Specifically, we develop a millimeter-wave radar system to contactlessly measure cardiac mechanical activity and reconstruct ECG without any contact in. To measure the cardiac mechanical activity comprehensively, we propose a series of signal processing algorithms to extract 4D cardiac motions from radio frequency (RF) signals. Furthermore, we design a deep neural network to solve the cardiac related domain transformation problem and achieve end-to-end reconstruction mapping from RF input to the ECG output. The experimental results show that our contactless ECG measurements achieve timing accuracy of cardiac electrical events with median error below 14ms and morphology accuracy with median Pearson-Correlation of 90% and median Root-Mean-Square-Error of 0.081mv compared to the groudtruth ECG. These results indicate that the system enables the potential of contactless, continuous and accurate ECG monitoring.